This notebook details the processing, formatting and analysis of the data generated from the simulation runs that explored the parameter space.¶
Thejasvi Beleyur, Max Planck Institute for Ornithology, Seewiesen Last Updated : September 2019
In [14]:
import random
import dill
import datetime as dt
import glob
from joblib import Parallel, delayed
import matplotlib.pyplot as plt
import pandas as pd
import scipy.spatial as spatial
import sys
sys.path.append('..//CPN//')
import numpy as np
import statsmodels.api as sm
from tqdm import tqdm_notebook, tqdm
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%matplotlib notebook
In [52]:
## A series of functions to parse the simulation output
def get_run_uuid(sim_output, **kwargs):
sim_id, sim_data = sim_output
return(sim_id['uuid'])
def get_run_random_seed(sim_output, **kwargs):
'''
'''
sim_ids, sim_data = sim_output
random_seed = sim_ids['np.random.seed']
return(random_seed)
def get_num_echoes_heard(sim_output, **kwargs):
'''
'''
sim_ids, sim_data = sim_output
num_echoes_heard = np.sum(sim_data[0])
return(num_echoes_heard)
which_echo = {True: 1 , False:0}
def get_echoids(sim_data, **kwargs):
'''
'''
heard = kwargs.get('heard', True)
echo_indices = np.argwhere(sim_data[0]==which_echo[heard]).flatten()
return(echo_indices)
def get_echo_levels(sim_output, **kwargs):
'''
'''
heard = kwargs.get('heard', True)
sim_ids, sim_data = sim_output
echo_ids = get_echoids(sim_data, **kwargs)
echo_levels = sim_data[1]['target_echoes'].loc[echo_ids,'level']
return(echo_levels)
def get_echo_direction(sim_output, **kwargs):
heard = kwargs.get('heard', True)
sim_ids, sim_data = sim_output
echo_ids = get_echoids(sim_data, **kwargs)
echo_direction = sim_data[1]['target_echoes'].loc[echo_ids,'theta']
return(echo_direction)
def get_group_size(sim_output, **kwargs):
''' This function is necessary because of the
stupid way I stored the parameter sets using classes
'''
sim_ids, sim_data = sim_output
num_bats_in_group = sim_data[0].size +1
return(num_bats_in_group)
def split_by_groupsize(df):
all_subdfs = []
group_sizes = np.unique(df['groupsize'])
for each_groupsize in group_sizes:
subdf = df[df['groupsize']==each_groupsize]
all_subdfs.append(subdf)
return(group_sizes, all_subdfs)
def get_individual_positions(sim_output, **kwargs):
'''
'''
sim_ids, sim_data = sim_output
_, _b, geometry = sim_data
positions = geometry['positions']
return(positions)
def get_detection_distance(sim_output, **kwargs):
'''
'''
heard = kwargs.get('heard', True)
sim_ids, sim_data = sim_output
echo_inds = get_echoids(sim_data, **kwargs)
individuals_inds = echo_inds +1 # because focal individ is 0 index
all_positions = get_individual_positions(sim_output)
heard_individual_positions = all_positions[individuals_inds,:]
focal_ind = all_positions[0,:]
distances = spatial.distance
positions_of_relevance = np.row_stack((focal_ind, heard_individual_positions))
distances = spatial.distance_matrix(positions_of_relevance,
positions_of_relevance)[1:,0]
return(distances)
def get_nearest_neighbour_distances(sim_output, **kwargs):
'''Extract the distance to the nearest neighbour of the focal bat
Parameters
-----------
sim_output : output from a simulation run.
Keyword Arguments
------------------
nearest_nbrs : int.
The number of distance measurements given.
Defaults to 5.
Returns
---------
nearest_neighbour_distances : 1 x nearest_nbrs np.array
'''
nearest_nbrs = kwargs.get('nearest_nbrs',5)
positions = get_individual_positions(sim_output)
distances = spatial.distance_matrix(positions, positions)[1:,0]
nearest_neighbour_distances = np.sort(distances)[:nearest_nbrs]
return(nearest_neighbour_distances)
def get_furthest_bat2bat_distance(sim_output, **kwargs):
'''
'''
positions = get_individual_positions(sim_output)
distances = spatial.distance_matrix(positions, positions)
furthest_distance = np.max(distances)
return(furthest_distance)
def extract_parameter_values(one_sim_result, **kwargs):
'''
Extracts the variables from the simulation result
by extracting the values from the
'parameter set'
Parameters
-----------
one_sim_results : tuple/list with 2 entries.
entry 1 should have the simulation identifiers
entry 2 may be anything.
Keyword Arguments
-----------------
variables_to_extract : list with str.
The names of the variables that are to be extracted.
Notes:
If 'source_level' is one of the variables - only the
emitted levels as dBSPL is output - the reference distance
is *ignored*.
Returns
--------
param_set : list.
A list with the numeric or Boolean values of each of the variables extracted.
'''
sim_identifiers, sim_data = one_sim_result
all_parameter_values = sim_identifiers['parameter_set']
param_set_for_this_run = []
for each in kwargs['variables_to_extract']:
if each != 'source_level':
param_set_for_this_run.append(all_parameter_values[each])
elif each == 'source_level':
param_set_for_this_run.append(all_parameter_values[each]['dBSPL'])
return(param_set_for_this_run)
make_to_string = lambda X: str(X)
def join_all_parameters(parameter_list):
'''
'''
params_as_string = map(make_to_string, parameter_list)
param_joined = '*'.join(params_as_string)
return(param_joined)
def make_paramset_id(sim_output, **kwargs):
'''
'''
all_parameter_values = extract_parameter_values(sim_output, **kwargs)
param_id = join_all_parameters(all_parameter_values)
return(param_id)
def load_simresult(path_to_simresult):
'''
'''
with open(path_to_simresult, 'rb') as sim:
output = dill.load(sim)
return(output)
def load_and_extract(simresult_path, extraction_functions, **kwargs):
'''
Parameters
----------
simresult_path : str/path object
extraction_functions : list/tuple with one or more function that work on
the simulation output
Keyword Arguments
-----------------
As defined by the extraction functions.
Every key must be unique and correspond to a particular extraction function!
Returns
---------
extracted_output : str OR object type thats returned by the extraction function
If the loading or extraction fails then
the path of file is returned.
'''
try:
simresult = load_simresult(simresult_path)
extracted_outputs = [ extract(simresult, **kwargs) for extract in extraction_functions]
return(extracted_outputs)
except:
return(simresult_path)
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# Some functions that deal with formatting the simulation data after it's been loaded from the csv file.
def format_nearest_neighbour_distances(nearest_nbr_entry):
'''
Parameters
----------
nearest_nbr_entry : string.
pd.DataFrame column entry with
the following format
'[<float>, <float>, <float>]'
Returns
-------
np.array
'''
only_float_as_string = nearest_nbr_entry[1:-1]
all_strings_separated = only_float_as_string.split()
floats = map(lambda X : float(X), all_strings_separated)
distances = np.array(floats)
return(distances)
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### Load the data : this needs a decent amount of RAM !! and takes some time - remember this.
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results_folder = '..//simulations/multivariable_simulations//'
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all_results = glob.glob(results_folder+'*.simresults')
some_results =random.sample(all_results, int(len(all_results)*1.0))
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extraction_fns = [get_num_echoes_heard, get_group_size, get_furthest_bat2bat_distance,
get_nearest_neighbour_distances, get_detection_distance, get_run_uuid,
get_run_random_seed, make_paramset_id, extract_parameter_values,
get_echo_levels, get_echo_direction]
In [31]:
keyword_arguments = {'nearest_nbrs':3}
keyword_arguments['variables_to_extract'] = ['heading_variation', 'echocall_duration','atmospheric_attenuation','min_spacing','source_level',
'interpulse_interval', 'implement_shadowing']
In [59]:
#### Un comment into Python cells if you need to re-run the analysis by loading the raw data
#### this can take 45mins-1 hour even when done in parallel
%time extracted_simdata = Parallel(n_jobs=8)(delayed(load_and_extract)(each, extraction_fns, **keyword_arguments) for each in tqdm(some_results))
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44.82it/s] 0%| | 1063/240584 [00:33<1:36:18, 41.45it/s] 0%| | 1069/240584 [00:33<1:31:27, 43.64it/s] 0%| | 1075/240584 [00:33<1:24:36, 47.18it/s] 0%| | 1080/240584 [00:33<1:34:17, 42.33it/s] 0%| | 1086/240584 [00:33<1:31:26, 43.65it/s] 0%| | 1094/240584 [00:33<1:19:15, 50.37it/s] 0%| | 1100/240584 [00:34<1:27:24, 45.67it/s] 0%| | 1106/240584 [00:34<1:25:11, 46.85it/s] 0%| | 1112/240584 [00:34<1:21:29, 48.98it/s] 0%| | 1118/240584 [00:34<1:24:59, 46.96it/s] 0%| | 1124/240584 [00:34<1:27:31, 45.60it/s] 0%| | 1131/240584 [00:34<1:19:24, 50.26it/s] 0%| | 1137/240584 [00:34<1:21:07, 49.19it/s] 0%| | 1143/240584 [00:34<1:20:05, 49.82it/s] 0%| | 1149/240584 [00:35<1:25:20, 46.76it/s] 0%| | 1154/240584 [00:35<1:35:34, 41.75it/s] 0%| | 1161/240584 [00:35<1:24:43, 47.10it/s] 0%| | 1167/240584 [00:35<1:29:58, 44.35it/s] 0%| | 1172/240584 [00:35<1:27:36, 45.55it/s] 0%| | 1177/240584 [00:35<1:28:07, 45.28it/s] 0%| | 1183/240584 [00:35<1:24:40, 47.12it/s] 0%| | 1188/240584 [00:36<1:38:06, 40.67it/s] 0%| | 1193/240584 [00:36<1:32:59, 42.91it/s] 0%| | 1198/240584 [00:36<1:29:27, 44.60it/s] 1%| | 1203/240584 [00:36<1:33:13, 42.80it/s] 1%| | 1209/240584 [00:36<1:28:44, 44.96it/s] 1%| | 1215/240584 [00:36<1:27:12, 45.75it/s] 1%| | 1222/240584 [00:36<1:20:28, 49.57it/s] 1%| | 1228/240584 [00:36<1:26:45, 45.98it/s] 1%| | 1233/240584 [00:36<1:25:31, 46.65it/s] 1%| | 1238/240584 [00:37<1:28:53, 44.88it/s] 1%| | 1243/240584 [00:37<1:43:23, 38.58it/s] 1%| | 1252/240584 [00:37<1:29:37, 44.50it/s] 1%| | 1257/240584 [00:37<1:28:49, 44.91it/s] 1%| | 1263/240584 [00:37<1:25:17, 46.77it/s] 1%| | 1268/240584 [00:37<1:24:02, 47.46it/s] 1%| | 1274/240584 [00:37<1:22:22, 48.42it/s] 1%| | 1279/240584 [00:37<1:28:59, 44.81it/s] 1%| | 1285/240584 [00:38<1:26:22, 46.18it/s] 1%| | 1290/240584 [00:38<1:29:48, 44.41it/s] 1%| | 1295/240584 [00:38<1:32:24, 43.16it/s] 1%| | 1301/240584 [00:38<1:29:15, 44.68it/s] 1%| | 1308/240584 [00:38<1:24:56, 46.95it/s] 1%| | 1313/240584 [00:38<1:27:27, 45.60it/s] 1%| | 1318/240584 [00:38<1:30:40, 43.98it/s] 1%| | 1323/240584 [00:38<1:41:28, 39.30it/s] 1%| | 1328/240584 [00:39<1:37:05, 41.07it/s] 1%| | 1333/240584 [00:39<1:39:17, 40.16it/s] 1%| | 1338/240584 [00:39<1:36:58, 41.12it/s] 1%| | 1345/240584 [00:39<1:26:47, 45.94it/s] 1%| | 1350/240584 [00:39<1:32:29, 43.11it/s] 1%| | 1357/240584 [00:39<1:23:19, 47.85it/s] 1%| | 1363/240584 [00:39<1:28:31, 45.03it/s] 1%| | 1368/240584 [00:39<1:29:03, 44.77it/s] 1%| | 1373/240584 [00:40<1:29:15, 44.66it/s] 1%| | 1379/240584 [00:40<1:25:02, 46.88it/s] 1%| | 1385/240584 [00:40<1:26:28, 46.10it/s] 1%| | 1391/240584 [00:40<1:22:04, 48.58it/s] 1%| | 1397/240584 [00:40<1:17:58, 51.12it/s] 1%| | 1403/240584 [00:40<1:23:04, 47.98it/s] 1%| | 1408/240584 [00:40<1:33:44, 42.53it/s] 1%| | 1414/240584 [00:40<1:26:57, 45.84it/s] 1%| | 1419/240584 [00:41<1:25:25, 46.67it/s] 1%| | 1424/240584 [00:41<1:25:04, 46.85it/s] 1%| | 1429/240584 [00:41<1:28:36, 44.98it/s] 1%| | 1436/240584 [00:41<1:22:14, 48.47it/s] 1%| | 1442/240584 [00:41<1:23:09, 47.93it/s] 1%| | 1450/240584 [00:41<1:16:18, 52.23it/s] 1%| | 1456/240584 [00:41<1:23:28, 47.74it/s] 1%| | 1461/240584 [00:41<1:28:20, 45.11it/s] 1%| | 1466/240584 [00:42<1:33:04, 42.82it/s] 1%| | 1473/240584 [00:42<1:22:43, 48.17it/s] 1%| | 1479/240584 [00:42<1:21:41, 48.78it/s] 1%| | 1485/240584 [00:42<1:22:30, 48.29it/s] 1%| | 1490/240584 [00:42<1:31:12, 43.69it/s] 1%| | 1496/240584 [00:42<1:24:44, 47.02it/s] 1%| | 1501/240584 [00:42<1:27:43, 45.42it/s] 1%| | 1507/240584 [00:42<1:27:20, 45.62it/s] 1%| | 1514/240584 [00:42<1:19:04, 50.39it/s] 1%| | 1520/240584 [00:43<1:21:50, 48.69it/s] 1%| | 1526/240584 [00:43<1:24:22, 47.22it/s] 1%| | 1531/240584 [00:43<1:25:57, 46.35it/s] 1%| | 1536/240584 [00:43<1:33:56, 42.41it/s] 1%| | 1544/240584 [00:43<1:22:58, 48.01it/s] 1%| | 1550/240584 [00:43<1:30:04, 44.23it/s] 1%| | 1557/240584 [00:43<1:25:20, 46.68it/s] 1%| | 1564/240584 [00:43<1:17:26, 51.44it/s] 1%| | 1570/240584 [00:44<1:21:01, 49.16it/s] 1%| | 1576/240584 [00:44<1:18:56, 50.47it/s] 1%| | 1582/240584 [00:44<1:30:44, 43.89it/s] 1%| | 1590/240584 [00:44<1:23:43, 47.57it/s] 1%| | 1597/240584 [00:44<1:18:38, 50.65it/s] 1%| | 1603/240584 [00:44<1:18:18, 50.86it/s] 1%| | 1609/240584 [00:44<1:25:26, 46.62it/s] 1%| | 1616/240584 [00:45<1:23:16, 47.82it/s] 1%| | 1621/240584 [00:45<1:23:54, 47.47it/s] 1%| | 1627/240584 [00:45<1:23:04, 47.94it/s] 1%| | 1632/240584 [00:45<1:23:04, 47.94it/s] 1%| | 1637/240584 [00:45<1:25:19, 46.67it/s] 1%| | 1642/240584 [00:45<1:26:29, 46.05it/s] 1%| | 1648/240584 [00:45<1:25:30, 46.57it/s] 1%| | 1654/240584 [00:45<1:22:22, 48.35it/s] 1%| | 1660/240584 [00:46<1:24:29, 47.13it/s] 1%| | 1667/240584 [00:46<1:17:31, 51.36it/s] 1%| | 1673/240584 [00:46<1:17:29, 51.38it/s] 1%| | 1679/240584 [00:46<1:22:04, 48.52it/s] 1%| | 1684/240584 [00:46<1:23:44, 47.55it/s] 1%| | 1689/240584 [00:46<1:24:47, 46.96it/s] 1%| | 1695/240584 [00:46<1:20:50, 49.25it/s] 1%| | 1700/240584 [00:46<1:21:32, 48.83it/s] 1%| | 1705/240584 [00:46<1:27:40, 45.41it/s] 1%| | 1710/240584 [00:47<1:27:24, 45.55it/s] 1%| | 1716/240584 [00:47<1:21:40, 48.74it/s] 1%| | 1722/240584 [00:47<1:18:19, 50.83it/s] 1%| | 1728/240584 [00:47<1:26:18, 46.13it/s] 1%| | 1736/240584 [00:47<1:22:15, 48.39it/s] 1%| | 1742/240584 [00:47<1:18:03, 51.00it/s] 1%| | 1748/240584 [00:47<1:24:17, 47.23it/s] 1%| | 1756/240584 [00:47<1:19:00, 50.38it/s] 1%| | 1762/240584 [00:48<1:21:47, 48.66it/s] 1%| | 1767/240584 [00:48<1:28:15, 45.09it/s] 1%| | 1773/240584 [00:48<1:22:55, 48.00it/s] 1%| | 1778/240584 [00:48<1:30:28, 43.99it/s] 1%| | 1784/240584 [00:48<1:32:28, 43.04it/s] 1%| | 1790/240584 [00:48<1:27:13, 45.63it/s] 1%| | 1797/240584 [00:48<1:22:05, 48.48it/s] 1%| | 1804/240584 [00:48<1:16:43, 51.87it/s] 1%| | 1810/240584 [00:49<1:23:49, 47.47it/s] 1%| | 1816/240584 [00:49<1:19:56, 49.78it/s] 1%| | 1822/240584 [00:49<1:27:30, 45.48it/s] 1%| | 1827/240584 [00:49<1:28:42, 44.86it/s] 1%| | 1832/240584 [00:49<1:28:31, 44.95it/s] 1%| | 1838/240584 [00:49<1:30:56, 43.75it/s] 1%| | 1847/240584 [00:49<1:25:01, 46.80it/s] 1%| | 1855/240584 [00:50<1:17:46, 51.16it/s] 1%| | 1861/240584 [00:50<1:18:02, 50.99it/s] 1%| | 1867/240584 [00:50<1:25:09, 46.72it/s] 1%| | 1872/240584 [00:50<1:23:43, 47.52it/s] 1%| | 1877/240584 [00:50<1:29:30, 44.45it/s] 1%| | 1883/240584 [00:50<1:24:10, 47.26it/s] 1%| | 1888/240584 [00:50<1:30:52, 43.77it/s] 1%| | 1896/240584 [00:50<1:25:22, 46.60it/s] 1%| | 1902/240584 [00:51<1:26:48, 45.83it/s] 1%| | 1907/240584 [00:51<1:25:24, 46.57it/s] 1%| | 1912/240584 [00:51<1:24:42, 46.96it/s] 1%| | 1918/240584 [00:51<1:19:37, 49.95it/s] 1%| | 1924/240584 [00:51<1:27:06, 45.66it/s] 1%| | 1931/240584 [00:51<1:20:14, 49.56it/s] 1%| | 1937/240584 [00:51<1:19:44, 49.88it/s] 1%| | 1943/240584 [00:51<1:24:09, 47.26it/s] 1%| | 1948/240584 [00:51<1:23:31, 47.62it/s] 1%| | 1954/240584 [00:52<1:25:39, 46.43it/s] 1%| | 1960/240584 [00:52<1:20:05, 49.66it/s] 1%| | 1966/240584 [00:52<1:28:09, 45.11it/s] 1%| | 1971/240584 [00:52<1:33:40, 42.45it/s] 1%| | 1977/240584 [00:52<1:28:10, 45.10it/s] 1%| | 1984/240584 [00:52<1:19:33, 49.98it/s] 1%| | 1990/240584 [00:52<1:23:46, 47.46it/s] 1%| | 1995/240584 [00:52<1:24:09, 47.25it/s] 1%| | 2000/240584 [00:53<1:30:14, 44.06it/s] 1%| | 2005/240584 [00:53<1:28:31, 44.91it/s] 1%| | 2011/240584 [00:53<1:23:55, 47.38it/s] 1%| | 2016/240584 [00:53<1:23:56, 47.36it/s] 1%| | 2021/240584 [00:53<1:24:46, 46.90it/s] 1%| | 2026/240584 [00:53<1:23:50, 47.42it/s] 1%| | 2031/240584 [00:53<1:25:28, 46.51it/s] 1%| | 2036/240584 [00:53<1:25:26, 46.54it/s] 1%| | 2041/240584 [00:53<1:25:14, 46.64it/s] 1%| | 2046/240584 [00:54<1:27:47, 45.29it/s] 1%| | 2051/240584 [00:54<1:27:41, 45.33it/s] 1%| | 2056/240584 [00:54<1:29:54, 44.22it/s] 1%| | 2061/240584 [00:54<1:29:39, 44.34it/s] 1%| | 2067/240584 [00:54<1:29:43, 44.31it/s] 1%| | 2072/240584 [00:54<1:34:23, 42.12it/s] 1%| | 2079/240584 [00:54<1:25:23, 46.55it/s] 1%| | 2084/240584 [00:54<1:29:17, 44.51it/s] 1%| | 2089/240584 [00:55<1:31:34, 43.41it/s] 1%| | 2096/240584 [00:55<1:24:10, 47.22it/s] 1%| | 2101/240584 [00:55<1:24:49, 46.86it/s] 1%| | 2106/240584 [00:55<1:38:14, 40.46it/s] 1%| | 2114/240584 [00:55<1:29:44, 44.29it/s] 1%| | 2120/240584 [00:55<1:23:02, 47.86it/s] 1%| | 2126/240584 [00:55<1:26:57, 45.70it/s] 1%| | 2131/240584 [00:55<1:28:38, 44.83it/s] 1%| | 2136/240584 [00:56<1:27:28, 45.43it/s] 1%| | 2141/240584 [00:56<1:32:48, 42.82it/s] 1%| | 2148/240584 [00:56<1:27:57, 45.18it/s] 1%| | 2155/240584 [00:56<1:20:06, 49.61it/s] 1%| | 2161/240584 [00:56<1:27:05, 45.62it/s] 1%| | 2167/240584 [00:56<1:24:56, 46.78it/s] 1%| | 2173/240584 [00:56<1:27:46, 45.27it/s] 1%| | 2178/240584 [00:56<1:31:56, 43.22it/s] 1%| | 2183/240584 [00:57<1:29:28, 44.40it/s] 1%| | 2189/240584 [00:57<1:26:40, 45.84it/s] 1%| | 2195/240584 [00:57<1:31:21, 43.49it/s] 1%| | 2202/240584 [00:57<1:30:18, 44.00it/s] 1%| | 2209/240584 [00:57<1:23:18, 47.69it/s] 1%| | 2214/240584 [00:57<1:27:01, 45.65it/s] 1%| | 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[06:43<1:29:57, 41.23it/s] 7%|▋ | 18035/240584 [06:43<1:28:00, 42.15it/s] 7%|▋ | 18040/240584 [06:43<1:26:59, 42.64it/s] 8%|▊ | 18045/240584 [06:43<1:28:15, 42.02it/s] 8%|▊ | 18050/240584 [06:44<1:28:29, 41.91it/s] 8%|▊ | 18055/240584 [06:44<1:31:15, 40.64it/s] 8%|▊ | 18060/240584 [06:44<1:31:12, 40.67it/s] 8%|▊ | 18065/240584 [06:44<1:36:21, 38.49it/s] 8%|▊ | 18071/240584 [06:44<1:29:21, 41.50it/s] 8%|▊ | 18076/240584 [06:44<1:30:02, 41.19it/s] 8%|▊ | 18081/240584 [06:44<1:35:29, 38.84it/s] 8%|▊ | 18086/240584 [06:45<1:37:59, 37.84it/s] 8%|▊ | 18092/240584 [06:45<1:34:01, 39.44it/s] 8%|▊ | 18097/240584 [06:45<1:33:23, 39.71it/s] 8%|▊ | 18102/240584 [06:45<1:37:40, 37.96it/s] 8%|▊ | 18108/240584 [06:45<1:35:21, 38.88it/s] 8%|▊ | 18114/240584 [06:45<1:25:38, 43.29it/s] 8%|▊ | 18119/240584 [06:45<1:31:00, 40.74it/s] 8%|▊ | 18124/240584 [06:45<1:34:50, 39.09it/s] 8%|▊ | 18130/240584 [06:46<1:30:31, 40.95it/s] 8%|▊ | 18135/240584 [06:46<1:25:41, 43.27it/s] 8%|▊ | 18140/240584 [06:46<1:26:28, 42.87it/s] 8%|▊ | 18145/240584 [06:46<1:32:16, 40.18it/s] 8%|▊ | 18150/240584 [06:46<1:28:35, 41.84it/s] 8%|▊ | 18155/240584 [06:46<1:26:36, 42.81it/s] 8%|▊ | 18160/240584 [06:46<1:30:44, 40.86it/s] 8%|▊ | 18165/240584 [06:46<1:31:30, 40.51it/s] 8%|▊ | 18170/240584 [06:47<1:29:40, 41.34it/s] 8%|▊ | 18175/240584 [06:47<1:40:36, 36.84it/s] 8%|▊ | 18183/240584 [06:47<1:25:43, 43.24it/s] 8%|▊ | 18188/240584 [06:47<1:31:58, 40.30it/s] 8%|▊ | 18193/240584 [06:47<1:31:46, 40.39it/s] 8%|▊ | 18198/240584 [06:47<1:28:51, 41.71it/s] 8%|▊ | 18203/240584 [06:47<1:30:34, 40.92it/s] 8%|▊ | 18208/240584 [06:47<1:29:51, 41.24it/s] 8%|▊ | 18213/240584 [06:48<1:34:09, 39.36it/s] 8%|▊ | 18218/240584 [06:48<1:31:06, 40.68it/s] 8%|▊ | 18223/240584 [06:48<1:31:15, 40.61it/s] 8%|▊ | 18228/240584 [06:48<1:27:09, 42.52it/s] 8%|▊ | 18233/240584 [06:48<1:40:24, 36.91it/s] 8%|▊ | 18239/240584 [06:48<1:30:16, 41.05it/s] 8%|▊ | 18244/240584 [06:48<1:38:40, 37.56it/s] 8%|▊ | 18249/240584 [06:48<1:39:35, 37.21it/s] 8%|▊ | 18253/240584 [06:49<1:38:17, 37.70it/s] 8%|▊ | 18259/240584 [06:49<1:35:10, 38.93it/s] 8%|▊ | 18264/240584 [06:49<1:34:03, 39.39it/s] 8%|▊ | 18269/240584 [06:49<1:41:27, 36.52it/s] 8%|▊ | 18275/240584 [06:49<1:33:45, 39.52it/s] 8%|▊ | 18280/240584 [06:49<1:31:24, 40.53it/s] 8%|▊ | 18285/240584 [06:49<1:35:55, 38.62it/s] 8%|▊ | 18291/240584 [06:50<1:28:27, 41.88it/s] 8%|▊ | 18297/240584 [06:50<1:24:25, 43.88it/s] 8%|▊ | 18302/240584 [06:50<1:37:54, 37.84it/s] 8%|▊ | 18308/240584 [06:50<1:30:19, 41.01it/s] 8%|▊ | 18313/240584 [06:50<1:30:42, 40.84it/s] 8%|▊ | 18319/240584 [06:50<1:23:42, 44.25it/s] 8%|▊ | 18324/240584 [06:50<1:35:42, 38.70it/s] 8%|▊ | 18329/240584 [06:50<1:34:22, 39.25it/s] 8%|▊ | 18334/240584 [06:51<1:37:15, 38.09it/s] 8%|▊ | 18339/240584 [06:51<1:32:46, 39.93it/s] 8%|▊ | 18344/240584 [06:51<1:33:16, 39.71it/s] 8%|▊ | 18349/240584 [06:51<1:31:54, 40.30it/s] 8%|▊ | 18354/240584 [06:51<1:35:49, 38.65it/s] 8%|▊ | 18359/240584 [06:51<1:39:30, 37.22it/s] 8%|▊ | 18365/240584 [06:51<1:29:53, 41.20it/s] 8%|▊ | 18370/240584 [06:51<1:33:19, 39.68it/s] 8%|▊ | 18375/240584 [06:52<1:37:07, 38.13it/s] 8%|▊ | 18382/240584 [06:52<1:25:49, 43.15it/s] 8%|▊ | 18387/240584 [06:52<1:29:34, 41.35it/s] 8%|▊ | 18392/240584 [06:52<1:41:52, 36.35it/s] 8%|▊ | 18398/240584 [06:52<1:32:13, 40.15it/s] 8%|▊ | 18403/240584 [06:52<1:35:44, 38.68it/s] 8%|▊ | 18408/240584 [06:52<1:29:33, 41.35it/s] 8%|▊ | 18413/240584 [06:53<1:29:54, 41.19it/s] 8%|▊ | 18419/240584 [06:53<1:32:08, 40.19it/s] 8%|▊ | 18424/240584 [06:53<1:28:38, 41.77it/s] 8%|▊ | 18429/240584 [06:53<1:29:52, 41.20it/s] 8%|▊ | 18434/240584 [06:53<1:30:24, 40.95it/s] 8%|▊ | 18439/240584 [06:53<1:34:29, 39.18it/s] 8%|▊ | 18444/240584 [06:53<1:34:40, 39.11it/s] 8%|▊ | 18450/240584 [06:53<1:24:51, 43.63it/s] 8%|▊ | 18455/240584 [06:54<1:34:30, 39.17it/s] 8%|▊ | 18460/240584 [06:54<1:34:02, 39.37it/s] 8%|▊ | 18465/240584 [06:54<1:30:53, 40.73it/s] 8%|▊ | 18471/240584 [06:54<1:25:46, 43.16it/s] 8%|▊ | 18476/240584 [06:54<1:28:32, 41.81it/s] 8%|▊ | 18481/240584 [06:54<1:34:07, 39.33it/s] 8%|▊ | 18486/240584 [06:54<1:30:16, 41.00it/s] 8%|▊ | 18491/240584 [06:54<1:35:23, 38.80it/s] 8%|▊ | 18497/240584 [06:55<1:30:17, 41.00it/s] 8%|▊ | 18502/240584 [06:55<1:35:17, 38.84it/s] 8%|▊ | 18508/240584 [06:55<1:30:30, 40.89it/s] 8%|▊ | 18515/240584 [06:55<1:29:31, 41.34it/s] 8%|▊ | 18520/240584 [06:55<1:30:31, 40.89it/s] 8%|▊ | 18525/240584 [06:55<1:32:52, 39.85it/s] 8%|▊ | 18530/240584 [06:55<1:37:53, 37.81it/s] 8%|▊ | 18534/240584 [06:56<1:42:23, 36.14it/s] 8%|▊ | 18539/240584 [06:56<1:35:32, 38.74it/s] 8%|▊ | 18545/240584 [06:56<1:25:56, 43.06it/s] 8%|▊ | 18550/240584 [06:56<1:35:07, 38.90it/s] 8%|▊ | 18556/240584 [06:56<1:26:08, 42.95it/s] 8%|▊ | 18561/240584 [06:56<1:28:07, 41.99it/s] 8%|▊ | 18566/240584 [06:56<1:29:58, 41.12it/s] 8%|▊ | 18571/240584 [06:56<1:32:45, 39.89it/s] 8%|▊ | 18577/240584 [06:57<1:27:08, 42.46it/s] 8%|▊ | 18582/240584 [06:57<1:28:15, 41.92it/s] 8%|▊ | 18587/240584 [06:57<1:30:09, 41.04it/s] 8%|▊ | 18592/240584 [06:57<1:27:05, 42.48it/s] 8%|▊ | 18597/240584 [06:57<1:26:02, 43.00it/s] 8%|▊ | 18602/240584 [06:57<1:32:14, 40.11it/s] 8%|▊ | 18607/240584 [06:57<1:33:50, 39.43it/s] 8%|▊ | 18613/240584 [06:57<1:25:12, 43.42it/s] 8%|▊ | 18618/240584 [06:58<1:27:10, 42.44it/s] 8%|▊ | 18623/240584 [06:58<1:29:36, 41.29it/s] 8%|▊ | 18628/240584 [06:58<1:35:15, 38.84it/s] 8%|▊ | 18635/240584 [06:58<1:24:03, 44.01it/s] 8%|▊ | 18640/240584 [06:58<1:31:20, 40.50it/s] 8%|▊ | 18645/240584 [06:58<1:30:18, 40.96it/s] 8%|▊ | 18651/240584 [06:58<1:23:58, 44.05it/s] 8%|▊ | 18656/240584 [06:58<1:31:08, 40.58it/s] 8%|▊ | 18661/240584 [06:59<1:33:37, 39.50it/s] 8%|▊ | 18666/240584 [06:59<1:28:38, 41.73it/s] 8%|▊ | 18671/240584 [06:59<1:35:46, 38.61it/s] 8%|▊ | 18677/240584 [06:59<1:30:52, 40.70it/s] 8%|▊ | 18682/240584 [06:59<1:27:06, 42.45it/s] 8%|▊ | 18687/240584 [06:59<1:25:27, 43.28it/s] 8%|▊ | 18692/240584 [06:59<1:27:52, 42.09it/s] 8%|▊ | 18698/240584 [06:59<1:22:18, 44.93it/s] 8%|▊ | 18703/240584 [07:00<1:24:17, 43.87it/s] 8%|▊ | 18709/240584 [07:00<1:23:25, 44.33it/s] 8%|▊ | 18714/240584 [07:00<1:31:11, 40.55it/s] 8%|▊ | 18719/240584 [07:00<1:27:32, 42.24it/s] 8%|▊ | 18724/240584 [07:00<1:25:20, 43.33it/s] 8%|▊ | 18729/240584 [07:00<1:34:26, 39.15it/s] 8%|▊ | 18735/240584 [07:00<1:28:41, 41.69it/s] 8%|▊ | 18740/240584 [07:00<1:34:05, 39.29it/s] 8%|▊ | 18745/240584 [07:01<1:34:25, 39.16it/s] 8%|▊ | 18750/240584 [07:01<1:31:45, 40.29it/s] 8%|▊ | 18755/240584 [07:01<1:35:20, 38.78it/s] 8%|▊ | 18759/240584 [07:01<1:39:39, 37.10it/s] 8%|▊ | 18763/240584 [07:01<1:40:32, 36.77it/s] 8%|▊ | 18768/240584 [07:01<1:35:24, 38.75it/s] 8%|▊ | 18772/240584 [07:01<1:38:48, 37.41it/s] 8%|▊ | 18777/240584 [07:01<1:36:28, 38.32it/s] 8%|▊ | 18781/240584 [07:02<1:41:09, 36.54it/s] 8%|▊ | 18785/240584 [07:02<1:42:58, 35.90it/s] 8%|▊ | 18790/240584 [07:02<1:34:26, 39.14it/s] 8%|▊ | 18795/240584 [07:02<1:35:16, 38.80it/s] 8%|▊ | 18799/240584 [07:02<1:35:02, 38.89it/s] 8%|▊ | 18805/240584 [07:02<1:33:42, 39.45it/s] 8%|▊ | 18811/240584 [07:02<1:27:23, 42.30it/s] 8%|▊ | 18816/240584 [07:02<1:35:00, 38.90it/s] 8%|▊ | 18823/240584 [07:02<1:23:54, 44.04it/s] 8%|▊ | 18828/240584 [07:03<1:33:52, 39.37it/s] 8%|▊ | 18833/240584 [07:03<1:33:13, 39.65it/s] 8%|▊ | 18840/240584 [07:03<1:26:45, 42.60it/s] 8%|▊ | 18846/240584 [07:03<1:24:46, 43.59it/s] 8%|▊ | 18851/240584 [07:03<1:23:28, 44.27it/s] 8%|▊ | 18856/240584 [07:03<1:32:33, 39.93it/s] 8%|▊ | 18861/240584 [07:03<1:30:35, 40.79it/s] 8%|▊ | 18866/240584 [07:04<1:31:54, 40.21it/s] 8%|▊ | 18871/240584 [07:04<1:29:54, 41.10it/s] 8%|▊ | 18876/240584 [07:04<1:33:24, 39.56it/s] 8%|▊ | 18881/240584 [07:04<1:32:12, 40.08it/s] 8%|▊ | 18886/240584 [07:04<1:38:18, 37.59it/s] 8%|▊ | 18891/240584 [07:04<1:33:29, 39.52it/s] 8%|▊ | 18896/240584 [07:04<1:38:14, 37.61it/s] 8%|▊ | 18901/240584 [07:04<1:35:23, 38.73it/s] 8%|▊ | 18907/240584 [07:05<1:27:37, 42.16it/s] 8%|▊ | 18912/240584 [07:05<1:37:16, 37.98it/s] 8%|▊ | 18918/240584 [07:05<1:32:46, 39.82it/s] 8%|▊ | 18923/240584 [07:05<1:28:10, 41.90it/s] 8%|▊ | 18928/240584 [07:05<1:33:20, 39.58it/s] 8%|▊ | 18934/240584 [07:05<1:36:15, 38.38it/s] 8%|▊ | 18941/240584 [07:05<1:25:26, 43.24it/s] 8%|▊ | 18946/240584 [07:06<1:25:13, 43.34it/s] 8%|▊ | 18951/240584 [07:06<1:25:55, 42.99it/s] 8%|▊ | 18956/240584 [07:06<1:31:20, 40.44it/s] 8%|▊ | 18961/240584 [07:06<1:28:00, 41.97it/s] 8%|▊ | 18966/240584 [07:06<1:35:27, 38.69it/s] 8%|▊ | 18970/240584 [07:06<1:37:29, 37.89it/s] 8%|▊ | 18977/240584 [07:06<1:33:16, 39.60it/s] 8%|▊ | 18983/240584 [07:06<1:25:05, 43.40it/s] 8%|▊ | 18988/240584 [07:07<1:28:42, 41.63it/s] 8%|▊ | 18993/240584 [07:07<1:33:12, 39.62it/s] 8%|▊ | 18999/240584 [07:07<1:29:41, 41.18it/s] 8%|▊ | 19004/240584 [07:07<1:32:46, 39.81it/s] 8%|▊ | 19009/240584 [07:07<1:29:05, 41.45it/s] 8%|▊ | 19014/240584 [07:07<1:26:21, 42.76it/s] 8%|▊ | 19019/240584 [07:07<1:23:40, 44.13it/s] 8%|▊ | 19024/240584 [07:07<1:28:42, 41.62it/s] 8%|▊ | 19029/240584 [07:08<1:27:51, 42.03it/s] 8%|▊ | 19034/240584 [07:08<1:29:20, 41.33it/s] 8%|▊ | 19040/240584 [07:08<1:27:23, 42.25it/s] 8%|▊ | 19045/240584 [07:08<1:27:05, 42.39it/s] 8%|▊ | 19050/240584 [07:08<1:26:47, 42.54it/s] 8%|▊ | 19055/240584 [07:08<1:25:28, 43.19it/s] 8%|▊ | 19060/240584 [07:08<1:28:15, 41.83it/s] 8%|▊ | 19065/240584 [07:08<1:27:16, 42.31it/s] 8%|▊ | 19070/240584 [07:08<1:26:05, 42.88it/s] 8%|▊ | 19075/240584 [07:09<1:33:00, 39.69it/s] 8%|▊ | 19080/240584 [07:09<1:31:32, 40.32it/s] 8%|▊ | 19086/240584 [07:09<1:26:11, 42.83it/s] 8%|▊ | 19091/240584 [07:09<1:30:14, 40.91it/s] 8%|▊ | 19097/240584 [07:09<1:25:22, 43.24it/s] 8%|▊ | 19102/240584 [07:09<1:32:11, 40.04it/s] 8%|▊ | 19107/240584 [07:09<1:30:00, 41.01it/s] 8%|▊ | 19112/240584 [07:09<1:25:55, 42.96it/s] 8%|▊ | 19117/240584 [07:10<1:32:53, 39.74it/s] 8%|▊ | 19122/240584 [07:10<1:40:59, 36.55it/s] 8%|▊ | 19129/240584 [07:10<1:30:51, 40.62it/s] 8%|▊ | 19134/240584 [07:10<1:33:25, 39.51it/s] 8%|▊ | 19139/240584 [07:10<1:28:41, 41.61it/s] 8%|▊ | 19144/240584 [07:10<1:42:44, 35.92it/s] 8%|▊ | 19150/240584 [07:10<1:36:18, 38.32it/s] 8%|▊ | 19155/240584 [07:11<1:29:52, 41.06it/s] 8%|▊ | 19160/240584 [07:11<1:27:53, 41.99it/s] 8%|▊ | 19165/240584 [07:11<1:31:24, 40.37it/s] 8%|▊ | 19170/240584 [07:11<1:29:14, 41.35it/s] 8%|▊ | 19175/240584 [07:11<1:25:51, 42.98it/s] 8%|▊ | 19180/240584 [07:11<1:26:49, 42.50it/s] 8%|▊ | 19185/240584 [07:11<1:23:42, 44.08it/s] 8%|▊ | 19190/240584 [07:11<1:28:45, 41.57it/s] 8%|▊ | 19195/240584 [07:12<1:31:28, 40.34it/s] 8%|▊ | 19200/240584 [07:12<1:35:59, 38.44it/s] 8%|▊ | 19204/240584 [07:12<1:36:15, 38.33it/s] 8%|▊ | 19209/240584 [07:12<1:33:26, 39.48it/s] 8%|▊ | 19215/240584 [07:12<1:25:56, 42.93it/s] 8%|▊ | 19220/240584 [07:12<1:25:48, 42.99it/s] 8%|▊ | 19225/240584 [07:12<1:28:38, 41.62it/s] 8%|▊ | 19230/240584 [07:12<1:33:15, 39.56it/s] 8%|▊ | 19235/240584 [07:13<1:28:55, 41.49it/s] 8%|▊ | 19240/240584 [07:13<1:31:58, 40.11it/s] 8%|▊ | 19246/240584 [07:13<1:26:27, 42.67it/s] 8%|▊ | 19251/240584 [07:13<1:28:22, 41.74it/s] 8%|▊ | 19256/240584 [07:13<1:28:15, 41.80it/s] 8%|▊ | 19261/240584 [07:13<1:41:28, 36.35it/s] 8%|▊ | 19267/240584 [07:13<1:29:38, 41.15it/s] 8%|▊ | 19272/240584 [07:13<1:37:07, 37.98it/s] 8%|▊ | 19278/240584 [07:14<1:31:33, 40.29it/s] 8%|▊ | 19283/240584 [07:14<1:33:24, 39.49it/s] 8%|▊ | 19289/240584 [07:14<1:28:32, 41.66it/s] 8%|▊ | 19294/240584 [07:14<1:28:47, 41.54it/s] 8%|▊ | 19299/240584 [07:14<1:24:52, 43.45it/s] 8%|▊ | 19304/240584 [07:14<1:24:56, 43.42it/s] 8%|▊ | 19309/240584 [07:14<1:26:07, 42.82it/s] 8%|▊ | 19314/240584 [07:14<1:25:22, 43.19it/s] 8%|▊ | 19319/240584 [07:15<1:28:37, 41.61it/s] 8%|▊ | 19324/240584 [07:15<1:28:14, 41.79it/s] 8%|▊ | 19330/240584 [07:15<1:26:07, 42.81it/s] 8%|▊ | 19335/240584 [07:15<1:36:53, 38.06it/s] 8%|▊ | 19341/240584 [07:15<1:28:43, 41.56it/s] 8%|▊ | 19346/240584 [07:15<1:27:38, 42.07it/s] 8%|▊ | 19351/240584 [07:15<1:25:13, 43.26it/s] 8%|▊ | 19356/240584 [07:15<1:30:43, 40.64it/s] 8%|▊ | 19362/240584 [07:16<1:32:35, 39.82it/s] 8%|▊ | 19368/240584 [07:16<1:30:13, 40.86it/s] 8%|▊ | 19373/240584 [07:16<1:29:08, 41.36it/s] 8%|▊ | 19378/240584 [07:16<1:33:07, 39.59it/s] 8%|▊ | 19384/240584 [07:16<1:27:50, 41.97it/s] 8%|▊ | 19390/240584 [07:16<1:21:44, 45.10it/s] 8%|▊ | 19395/240584 [07:16<1:33:00, 39.64it/s] 8%|▊ | 19400/240584 [07:16<1:28:16, 41.76it/s] 8%|▊ | 19405/240584 [07:17<1:25:51, 42.94it/s] 8%|▊ | 19410/240584 [07:17<1:34:50, 38.87it/s] 8%|▊ | 19415/240584 [07:17<1:33:22, 39.48it/s] 8%|▊ | 19420/240584 [07:17<1:31:18, 40.37it/s] 8%|▊ | 19425/240584 [07:17<1:39:47, 36.94it/s] 8%|▊ | 19430/240584 [07:17<1:32:11, 39.98it/s] 8%|▊ | 19435/240584 [07:17<1:32:57, 39.65it/s] 8%|▊ | 19440/240584 [07:18<1:33:18, 39.50it/s] 8%|▊ | 19445/240584 [07:18<1:28:49, 41.49it/s] 8%|▊ | 19450/240584 [07:18<1:34:29, 39.01it/s] 8%|▊ | 19454/240584 [07:18<1:36:48, 38.07it/s] 8%|▊ | 19459/240584 [07:18<1:30:06, 40.90it/s] 8%|▊ | 19464/240584 [07:18<1:38:10, 37.54it/s] 8%|▊ | 19469/240584 [07:18<1:31:34, 40.24it/s] 8%|▊ | 19474/240584 [07:18<1:34:44, 38.90it/s] 8%|▊ | 19479/240584 [07:18<1:31:51, 40.12it/s] 8%|▊ | 19484/240584 [07:19<1:33:38, 39.35it/s] 8%|▊ | 19489/240584 [07:19<1:33:06, 39.58it/s] 8%|▊ | 19495/240584 [07:19<1:28:55, 41.44it/s] 8%|▊ | 19500/240584 [07:19<1:37:57, 37.62it/s] 8%|▊ | 19506/240584 [07:19<1:29:32, 41.15it/s] 8%|▊ | 19511/240584 [07:19<1:37:22, 37.84it/s] 8%|▊ | 19517/240584 [07:19<1:31:55, 40.08it/s] 8%|▊ | 19522/240584 [07:20<1:29:15, 41.28it/s] 8%|▊ | 19528/240584 [07:20<1:28:37, 41.57it/s] 8%|▊ | 19533/240584 [07:20<1:29:38, 41.10it/s] 8%|▊ | 19538/240584 [07:20<1:28:51, 41.46it/s] 8%|▊ | 19543/240584 [07:20<1:26:17, 42.69it/s] 8%|▊ | 19548/240584 [07:20<1:37:41, 37.71it/s] 8%|▊ | 19554/240584 [07:20<1:32:20, 39.89it/s] 8%|▊ | 19560/240584 [07:20<1:28:58, 41.40it/s] 8%|▊ | 19565/240584 [07:21<1:28:14, 41.74it/s] 8%|▊ | 19570/240584 [07:21<1:31:47, 40.13it/s] 8%|▊ | 19575/240584 [07:21<1:31:33, 40.23it/s] 8%|▊ | 19580/240584 [07:21<1:31:52, 40.09it/s] 8%|▊ | 19585/240584 [07:21<1:33:09, 39.54it/s] 8%|▊ | 19589/240584 [07:21<1:43:50, 35.47it/s] 8%|▊ | 19594/240584 [07:21<1:41:11, 36.40it/s] 8%|▊ | 19601/240584 [07:22<1:30:22, 40.75it/s] 8%|▊ | 19606/240584 [07:22<1:38:12, 37.50it/s] 8%|▊ | 19611/240584 [07:22<1:38:24, 37.42it/s] 8%|▊ | 19616/240584 [07:22<1:32:04, 40.00it/s] 8%|▊ | 19621/240584 [07:22<1:40:42, 36.57it/s] 8%|▊ | 19627/240584 [07:22<1:29:09, 41.30it/s] 8%|▊ | 19632/240584 [07:22<1:31:43, 40.15it/s] 8%|▊ | 19637/240584 [07:22<1:32:59, 39.60it/s] 8%|▊ | 19642/240584 [07:23<1:39:06, 37.15it/s] 8%|▊ | 19648/240584 [07:23<1:33:31, 39.37it/s] 8%|▊ | 19653/240584 [07:23<1:33:13, 39.50it/s] 8%|▊ | 19659/240584 [07:23<1:28:04, 41.81it/s] 8%|▊ | 19664/240584 [07:23<1:31:25, 40.27it/s] 8%|▊ | 19669/240584 [07:23<1:30:34, 40.65it/s] 8%|▊ | 19674/240584 [07:23<1:34:50, 38.82it/s] 8%|▊ | 19678/240584 [07:23<1:40:38, 36.58it/s] 8%|▊ | 19682/240584 [07:24<1:38:52, 37.23it/s] 8%|▊ | 19689/240584 [07:24<1:25:38, 42.99it/s] 8%|▊ | 19694/240584 [07:24<1:30:55, 40.49it/s] 8%|▊ | 19699/240584 [07:24<1:35:09, 38.68it/s] 8%|▊ | 19704/240584 [07:24<1:39:39, 36.94it/s] 8%|▊ | 19710/240584 [07:24<1:33:22, 39.42it/s] 8%|▊ | 19715/240584 [07:24<1:30:46, 40.55it/s] 8%|▊ | 19720/240584 [07:25<1:32:21, 39.85it/s] 8%|▊ | 19725/240584 [07:25<1:36:41, 38.07it/s] 8%|▊ | 19731/240584 [07:25<1:31:41, 40.14it/s] 8%|▊ | 19736/240584 [07:25<1:30:09, 40.83it/s] 8%|▊ | 19741/240584 [07:25<1:43:24, 35.59it/s] 8%|▊ | 19747/240584 [07:25<1:36:56, 37.97it/s] 8%|▊ | 19752/240584 [07:25<1:33:03, 39.55it/s] 8%|▊ | 19757/240584 [07:25<1:38:35, 37.33it/s] 8%|▊ | 19763/240584 [07:26<1:30:01, 40.88it/s] 8%|▊ | 19768/240584 [07:26<1:28:13, 41.71it/s] 8%|▊ | 19773/240584 [07:26<1:29:36, 41.07it/s] 8%|▊ | 19778/240584 [07:26<1:31:59, 40.00it/s] 8%|▊ | 19783/240584 [07:26<1:33:33, 39.34it/s] 8%|▊ | 19788/240584 [07:26<1:37:36, 37.70it/s] 8%|▊ | 19793/240584 [07:26<1:30:35, 40.62it/s] 8%|▊ | 19798/240584 [07:26<1:35:17, 38.62it/s] 8%|▊ | 19805/240584 [07:27<1:31:56, 40.02it/s] 8%|▊ | 19811/240584 [07:27<1:27:10, 42.21it/s] 8%|▊ | 19816/240584 [07:27<1:31:42, 40.12it/s] 8%|▊ | 19821/240584 [07:27<1:37:06, 37.89it/s] 8%|▊ | 19827/240584 [07:27<1:30:53, 40.48it/s] 8%|▊ | 19832/240584 [07:27<1:33:06, 39.51it/s] 8%|▊ | 19837/240584 [07:27<1:42:39, 35.84it/s] 8%|▊ | 19842/240584 [07:28<1:35:50, 38.39it/s] 8%|▊ | 19848/240584 [07:28<1:27:13, 42.18it/s] 8%|▊ | 19853/240584 [07:28<1:33:30, 39.34it/s] 8%|▊ | 19858/240584 [07:28<1:41:23, 36.28it/s] 8%|▊ | 19863/240584 [07:28<1:40:49, 36.48it/s] 8%|▊ | 19867/240584 [07:28<1:41:21, 36.29it/s] 8%|▊ | 19873/240584 [07:28<1:35:35, 38.48it/s] 8%|▊ | 19879/240584 [07:29<1:28:56, 41.36it/s] 8%|▊ | 19884/240584 [07:29<1:29:29, 41.10it/s] 8%|▊ | 19889/240584 [07:29<1:48:51, 33.79it/s] 8%|▊ | 19894/240584 [07:29<1:39:10, 37.09it/s] 8%|▊ | 19899/240584 [07:29<1:39:36, 36.93it/s] 8%|▊ | 19906/240584 [07:29<1:32:06, 39.93it/s] 8%|▊ | 19911/240584 [07:29<1:30:33, 40.62it/s] 8%|▊ | 19916/240584 [07:29<1:30:24, 40.68it/s] 8%|▊ | 19921/240584 [07:30<1:25:38, 42.95it/s] 8%|▊ | 19926/240584 [07:30<1:36:29, 38.11it/s] 8%|▊ | 19932/240584 [07:30<1:27:15, 42.15it/s] 8%|▊ | 19937/240584 [07:30<1:24:33, 43.49it/s] 8%|▊ | 19942/240584 [07:30<1:25:28, 43.02it/s] 8%|▊ | 19947/240584 [07:30<1:29:42, 40.99it/s] 8%|▊ | 19954/240584 [07:30<1:26:12, 42.66it/s] 8%|▊ | 19959/240584 [07:30<1:24:20, 43.60it/s] 8%|▊ | 19964/240584 [07:31<1:31:23, 40.23it/s] 8%|▊ | 19970/240584 [07:31<1:31:02, 40.39it/s] 8%|▊ | 19975/240584 [07:31<1:33:44, 39.22it/s] 8%|▊ | 19980/240584 [07:31<1:37:21, 37.77it/s] 8%|▊ | 19986/240584 [07:31<1:32:17, 39.84it/s] 8%|▊ | 19991/240584 [07:31<1:33:16, 39.42it/s] 8%|▊ | 19995/240584 [07:31<1:33:39, 39.25it/s] 8%|▊ | 20001/240584 [07:32<1:31:01, 40.39it/s] 8%|▊ | 20007/240584 [07:32<1:23:01, 44.28it/s] 8%|▊ | 20012/240584 [07:32<1:27:17, 42.12it/s] 8%|▊ | 20017/240584 [07:32<1:34:04, 39.08it/s] 8%|▊ | 20022/240584 [07:32<1:37:02, 37.88it/s] 8%|▊ | 20028/240584 [07:32<1:36:33, 38.07it/s] 8%|▊ | 20033/240584 [07:32<1:32:03, 39.93it/s] 8%|▊ | 20038/240584 [07:32<1:32:00, 39.95it/s] 8%|▊ | 20043/240584 [07:33<1:35:47, 38.37it/s] 8%|▊ | 20047/240584 [07:33<1:36:58, 37.91it/s] 8%|▊ | 20052/240584 [07:33<1:36:55, 37.92it/s] 8%|▊ | 20057/240584 [07:33<1:31:39, 40.10it/s] 8%|▊ | 20063/240584 [07:33<1:31:00, 40.39it/s] 8%|▊ | 20068/240584 [07:33<1:31:21, 40.23it/s] 8%|▊ | 20073/240584 [07:33<1:31:21, 40.23it/s] 8%|▊ | 20078/240584 [07:33<1:29:58, 40.85it/s] 8%|▊ | 20083/240584 [07:34<1:31:06, 40.34it/s] 8%|▊ | 20088/240584 [07:34<1:37:28, 37.70it/s] 8%|▊ | 20094/240584 [07:34<1:27:23, 42.05it/s] 8%|▊ | 20099/240584 [07:34<1:39:50, 36.81it/s] 8%|▊ | 20106/240584 [07:34<1:29:09, 41.21it/s] 8%|▊ | 20111/240584 [07:34<1:29:09, 41.21it/s] 8%|▊ | 20116/240584 [07:34<1:37:21, 37.74it/s] 8%|▊ | 20122/240584 [07:35<1:26:40, 42.39it/s] 8%|▊ | 20127/240584 [07:35<1:27:42, 41.89it/s] 8%|▊ | 20132/240584 [07:35<1:35:12, 38.59it/s] 8%|▊ | 20139/240584 [07:35<1:24:46, 43.34it/s] 8%|▊ | 20144/240584 [07:35<1:23:14, 44.13it/s] 8%|▊ | 20149/240584 [07:35<1:36:53, 37.92it/s] 8%|▊ | 20155/240584 [07:35<1:32:47, 39.59it/s] 8%|▊ | 20161/240584 [07:35<1:28:04, 41.71it/s] 8%|▊ | 20166/240584 [07:36<1:26:33, 42.44it/s] 8%|▊ | 20171/240584 [07:36<1:28:17, 41.61it/s] 8%|▊ | 20176/240584 [07:36<1:28:35, 41.46it/s] 8%|▊ | 20182/240584 [07:36<1:22:11, 44.69it/s] 8%|▊ | 20187/240584 [07:36<1:26:05, 42.67it/s] 8%|▊ | 20192/240584 [07:36<1:29:09, 41.20it/s] 8%|▊ | 20197/240584 [07:36<1:33:31, 39.27it/s] 8%|▊ | 20202/240584 [07:36<1:30:19, 40.66it/s] 8%|▊ | 20207/240584 [07:37<1:29:49, 40.89it/s] 8%|▊ | 20212/240584 [07:37<1:25:15, 43.08it/s] 8%|▊ | 20217/240584 [07:37<1:28:11, 41.65it/s] 8%|▊ | 20222/240584 [07:37<1:29:09, 41.19it/s] 8%|▊ | 20227/240584 [07:37<1:26:21, 42.52it/s] 8%|▊ | 20232/240584 [07:37<1:31:24, 40.18it/s] 8%|▊ | 20237/240584 [07:37<1:26:43, 42.35it/s] 8%|▊ | 20242/240584 [07:37<1:30:32, 40.56it/s] 8%|▊ | 20249/240584 [07:38<1:27:21, 42.03it/s] 8%|▊ | 20254/240584 [07:38<1:32:00, 39.91it/s] 8%|▊ | 20259/240584 [07:38<1:29:11, 41.17it/s] 8%|▊ | 20264/240584 [07:38<1:33:09, 39.41it/s] 8%|▊ | 20270/240584 [07:38<1:26:11, 42.60it/s] 8%|▊ | 20275/240584 [07:38<1:29:34, 40.99it/s] 8%|▊ | 20280/240584 [07:38<1:27:21, 42.03it/s] 8%|▊ | 20285/240584 [07:38<1:25:30, 42.94it/s] 8%|▊ | 20290/240584 [07:39<1:38:11, 37.39it/s] 8%|▊ | 20297/240584 [07:39<1:33:29, 39.27it/s] 8%|▊ | 20302/240584 [07:39<1:37:05, 37.82it/s] 8%|▊ | 20307/240584 [07:39<1:35:04, 38.61it/s] 8%|▊ | 20311/240584 [07:39<1:35:31, 38.43it/s] 8%|▊ | 20317/240584 [07:39<1:26:30, 42.43it/s] 8%|▊ | 20322/240584 [07:39<1:30:12, 40.70it/s] 8%|▊ | 20327/240584 [07:39<1:31:58, 39.91it/s] 8%|▊ | 20332/240584 [07:40<1:30:24, 40.60it/s] 8%|▊ | 20337/240584 [07:40<1:33:28, 39.27it/s] 8%|▊ | 20343/240584 [07:40<1:26:55, 42.23it/s] 8%|▊ | 20348/240584 [07:40<1:31:24, 40.16it/s] 8%|▊ | 20355/240584 [07:40<1:22:52, 44.29it/s] 8%|▊ | 20360/240584 [07:40<1:31:27, 40.13it/s] 8%|▊ | 20365/240584 [07:40<1:30:03, 40.76it/s] 8%|▊ | 20370/240584 [07:41<1:32:20, 39.75it/s] 8%|▊ | 20375/240584 [07:41<1:30:52, 40.38it/s] 8%|▊ | 20380/240584 [07:41<1:27:48, 41.80it/s] 8%|▊ | 20385/240584 [07:41<1:29:21, 41.07it/s] 8%|▊ | 20390/240584 [07:41<1:33:45, 39.14it/s] 8%|▊ | 20397/240584 [07:41<1:27:48, 41.79it/s] 8%|▊ | 20402/240584 [07:41<1:30:46, 40.43it/s] 8%|▊ | 20407/240584 [07:41<1:33:56, 39.06it/s] 8%|▊ | 20412/240584 [07:42<1:38:35, 37.22it/s] 8%|▊ | 20418/240584 [07:42<1:32:15, 39.77it/s] 8%|▊ | 20423/240584 [07:42<1:28:07, 41.64it/s] 8%|▊ | 20428/240584 [07:42<1:29:48, 40.86it/s] 8%|▊ | 20433/240584 [07:42<1:43:12, 35.55it/s] 8%|▊ | 20441/240584 [07:42<1:32:49, 39.53it/s] 8%|▊ | 20446/240584 [07:42<1:28:37, 41.40it/s] 9%|▊ | 20451/240584 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43613/240584 [17:41<1:24:10, 39.00it/s] 18%|█▊ | 43619/240584 [17:41<1:20:46, 40.64it/s] 18%|█▊ | 43625/240584 [17:41<1:17:05, 42.58it/s] 18%|█▊ | 43630/240584 [17:41<1:16:35, 42.86it/s] 18%|█▊ | 43635/240584 [17:41<1:19:17, 41.40it/s] 18%|█▊ | 43640/240584 [17:41<1:18:39, 41.73it/s] 18%|█▊ | 43645/240584 [17:42<1:18:11, 41.98it/s] 18%|█▊ | 43650/240584 [17:42<1:18:21, 41.88it/s] 18%|█▊ | 43655/240584 [17:42<1:26:07, 38.11it/s] 18%|█▊ | 43661/240584 [17:42<1:20:38, 40.70it/s] 18%|█▊ | 43666/240584 [17:42<1:21:12, 40.42it/s] 18%|█▊ | 43671/240584 [17:42<1:17:18, 42.45it/s] 18%|█▊ | 43676/240584 [17:42<1:18:17, 41.91it/s] 18%|█▊ | 43681/240584 [17:42<1:22:26, 39.80it/s] 18%|█▊ | 43686/240584 [17:43<1:22:56, 39.57it/s] 18%|█▊ | 43691/240584 [17:43<1:19:13, 41.42it/s] 18%|█▊ | 43696/240584 [17:43<1:18:12, 41.96it/s] 18%|█▊ | 43703/240584 [17:43<1:18:09, 41.99it/s] 18%|█▊ | 43708/240584 [17:43<1:16:15, 43.03it/s] 18%|█▊ | 43713/240584 [17:43<1:23:13, 39.43it/s] 18%|█▊ | 43718/240584 [17:43<1:21:14, 40.38it/s] 18%|█▊ | 43723/240584 [17:43<1:22:27, 39.79it/s] 18%|█▊ | 43730/240584 [17:44<1:22:13, 39.90it/s] 18%|█▊ | 43735/240584 [17:44<1:18:49, 41.62it/s] 18%|█▊ | 43740/240584 [17:44<1:20:32, 40.73it/s] 18%|█▊ | 43745/240584 [17:44<1:16:45, 42.74it/s] 18%|█▊ | 43750/240584 [17:44<1:17:10, 42.51it/s] 18%|█▊ | 43755/240584 [17:44<1:20:16, 40.86it/s] 18%|█▊ | 43760/240584 [17:44<1:22:48, 39.62it/s] 18%|█▊ | 43766/240584 [17:45<1:16:14, 43.02it/s] 18%|█▊ | 43771/240584 [17:45<1:22:46, 39.62it/s] 18%|█▊ | 43778/240584 [17:45<1:20:29, 40.75it/s] 18%|█▊ | 43784/240584 [17:45<1:19:44, 41.14it/s] 18%|█▊ | 43791/240584 [17:45<1:13:28, 44.63it/s] 18%|█▊ | 43796/240584 [17:45<1:11:53, 45.63it/s] 18%|█▊ | 43801/240584 [17:45<1:21:25, 40.28it/s] 18%|█▊ | 43807/240584 [17:45<1:13:36, 44.55it/s] 18%|█▊ | 43812/240584 [17:46<1:19:22, 41.32it/s] 18%|█▊ | 43817/240584 [17:46<1:15:58, 43.16it/s] 18%|█▊ | 43822/240584 [17:46<1:23:05, 39.47it/s] 18%|█▊ | 43827/240584 [17:46<1:22:49, 39.60it/s] 18%|█▊ | 43832/240584 [17:46<1:19:36, 41.19it/s] 18%|█▊ | 43837/240584 [17:46<1:23:59, 39.04it/s] 18%|█▊ | 43844/240584 [17:46<1:20:00, 40.99it/s] 18%|█▊ | 43850/240584 [17:46<1:15:40, 43.33it/s] 18%|█▊ | 43857/240584 [17:47<1:14:58, 43.73it/s] 18%|█▊ | 43862/240584 [17:47<1:19:03, 41.47it/s] 18%|█▊ | 43869/240584 [17:47<1:18:19, 41.86it/s] 18%|█▊ | 43874/240584 [17:47<1:15:05, 43.66it/s] 18%|█▊ | 43879/240584 [17:47<1:14:43, 43.88it/s] 18%|█▊ | 43884/240584 [17:47<1:28:35, 37.00it/s] 18%|█▊ | 43892/240584 [17:48<1:20:15, 40.84it/s] 18%|█▊ | 43898/240584 [17:48<1:16:05, 43.08it/s] 18%|█▊ | 43903/240584 [17:48<1:20:00, 40.97it/s] 18%|█▊ | 43908/240584 [17:48<1:21:09, 40.39it/s] 18%|█▊ | 43914/240584 [17:48<1:15:59, 43.13it/s] 18%|█▊ | 43919/240584 [17:48<1:17:04, 42.52it/s] 18%|█▊ | 43924/240584 [17:48<1:25:23, 38.38it/s] 18%|█▊ | 43931/240584 [17:48<1:23:24, 39.30it/s] 18%|█▊ | 43936/240584 [17:49<1:20:26, 40.75it/s] 18%|█▊ | 43942/240584 [17:49<1:15:38, 43.33it/s] 18%|█▊ | 43947/240584 [17:49<1:13:46, 44.42it/s] 18%|█▊ | 43952/240584 [17:49<1:15:02, 43.68it/s] 18%|█▊ | 43957/240584 [17:49<1:19:16, 41.34it/s] 18%|█▊ | 43962/240584 [17:49<1:17:06, 42.50it/s] 18%|█▊ | 43967/240584 [17:49<1:25:06, 38.50it/s] 18%|█▊ | 43972/240584 [17:49<1:19:52, 41.03it/s] 18%|█▊ | 43977/240584 [17:50<1:21:29, 40.21it/s] 18%|█▊ | 43982/240584 [17:50<1:17:55, 42.05it/s] 18%|█▊ | 43987/240584 [17:50<1:14:41, 43.86it/s] 18%|█▊ | 43993/240584 [17:50<1:23:16, 39.34it/s] 18%|█▊ | 43999/240584 [17:50<1:15:35, 43.35it/s] 18%|█▊ | 44004/240584 [17:50<1:23:11, 39.38it/s] 18%|█▊ | 44010/240584 [17:50<1:15:31, 43.38it/s] 18%|█▊ | 44015/240584 [17:50<1:20:13, 40.84it/s] 18%|█▊ | 44020/240584 [17:51<1:21:10, 40.36it/s] 18%|█▊ | 44025/240584 [17:51<1:19:08, 41.39it/s] 18%|█▊ | 44030/240584 [17:51<1:20:23, 40.75it/s] 18%|█▊ | 44036/240584 [17:51<1:15:24, 43.44it/s] 18%|█▊ | 44041/240584 [17:51<1:18:03, 41.96it/s] 18%|█▊ | 44046/240584 [17:51<1:19:24, 41.25it/s] 18%|█▊ | 44051/240584 [17:51<1:20:29, 40.70it/s] 18%|█▊ | 44057/240584 [17:51<1:16:16, 42.94it/s] 18%|█▊ | 44062/240584 [17:52<1:20:24, 40.74it/s] 18%|█▊ | 44067/240584 [17:52<1:18:21, 41.80it/s] 18%|█▊ | 44072/240584 [17:52<1:19:35, 41.15it/s] 18%|█▊ | 44077/240584 [17:52<1:27:09, 37.58it/s] 18%|█▊ | 44084/240584 [17:52<1:23:00, 39.46it/s] 18%|█▊ | 44090/240584 [17:52<1:15:05, 43.61it/s] 18%|█▊ | 44095/240584 [17:52<1:21:51, 40.01it/s] 18%|█▊ | 44100/240584 [17:53<1:26:36, 37.81it/s] 18%|█▊ | 44106/240584 [17:53<1:18:14, 41.85it/s] 18%|█▊ | 44111/240584 [17:53<1:16:37, 42.73it/s] 18%|█▊ | 44116/240584 [17:53<1:21:25, 40.21it/s] 18%|█▊ | 44121/240584 [17:53<1:24:55, 38.55it/s] 18%|█▊ | 44126/240584 [17:53<1:20:48, 40.52it/s] 18%|█▊ | 44131/240584 [17:53<1:17:41, 42.15it/s] 18%|█▊ | 44136/240584 [17:53<1:18:23, 41.77it/s] 18%|█▊ | 44143/240584 [17:54<1:16:13, 42.95it/s] 18%|█▊ | 44148/240584 [17:54<1:22:38, 39.62it/s] 18%|█▊ | 44153/240584 [17:54<1:22:43, 39.58it/s] 18%|█▊ | 44160/240584 [17:54<1:17:07, 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44272/240584 [17:57<1:17:26, 42.25it/s] 18%|█▊ | 44277/240584 [17:57<1:23:58, 38.96it/s] 18%|█▊ | 44283/240584 [17:57<1:19:42, 41.05it/s] 18%|█▊ | 44288/240584 [17:57<1:18:35, 41.63it/s] 18%|█▊ | 44293/240584 [17:57<1:28:38, 36.91it/s] 18%|█▊ | 44298/240584 [17:57<1:21:46, 40.01it/s] 18%|█▊ | 44304/240584 [17:57<1:17:32, 42.19it/s] 18%|█▊ | 44309/240584 [17:58<1:25:37, 38.20it/s] 18%|█▊ | 44314/240584 [17:58<1:26:53, 37.65it/s] 18%|█▊ | 44321/240584 [17:58<1:16:55, 42.53it/s] 18%|█▊ | 44326/240584 [17:58<1:21:56, 39.92it/s] 18%|█▊ | 44331/240584 [17:58<1:20:55, 40.42it/s] 18%|█▊ | 44337/240584 [17:58<1:15:33, 43.28it/s] 18%|█▊ | 44342/240584 [17:58<1:22:52, 39.46it/s] 18%|█▊ | 44347/240584 [17:58<1:21:10, 40.29it/s] 18%|█▊ | 44352/240584 [17:59<1:19:53, 40.94it/s] 18%|█▊ | 44357/240584 [17:59<1:28:04, 37.13it/s] 18%|█▊ | 44362/240584 [17:59<1:23:16, 39.28it/s] 18%|█▊ | 44367/240584 [17:59<1:20:19, 40.71it/s] 18%|█▊ | 44372/240584 [17:59<1:24:56, 38.50it/s] 18%|█▊ | 44376/240584 [17:59<1:25:24, 38.29it/s] 18%|█▊ | 44381/240584 [17:59<1:22:32, 39.62it/s] 18%|█▊ | 44386/240584 [17:59<1:26:21, 37.86it/s] 18%|█▊ | 44393/240584 [18:00<1:19:13, 41.27it/s] 18%|█▊ | 44398/240584 [18:00<1:16:33, 42.71it/s] 18%|█▊ | 44403/240584 [18:00<1:21:51, 39.95it/s] 18%|█▊ | 44409/240584 [18:00<1:18:01, 41.91it/s] 18%|█▊ | 44415/240584 [18:00<1:12:48, 44.91it/s] 18%|█▊ | 44420/240584 [18:00<1:22:18, 39.72it/s] 18%|█▊ | 44426/240584 [18:00<1:14:55, 43.63it/s] 18%|█▊ | 44431/240584 [18:00<1:15:15, 43.44it/s] 18%|█▊ | 44436/240584 [18:01<1:18:21, 41.72it/s] 18%|█▊ | 44441/240584 [18:01<1:25:53, 38.06it/s] 18%|█▊ | 44447/240584 [18:01<1:19:28, 41.13it/s] 18%|█▊ | 44452/240584 [18:01<1:17:49, 42.00it/s] 18%|█▊ | 44457/240584 [18:01<1:17:25, 42.22it/s] 18%|█▊ | 44462/240584 [18:01<1:18:14, 41.77it/s] 18%|█▊ | 44468/240584 [18:01<1:12:34, 45.04it/s] 18%|█▊ | 44473/240584 [18:01<1:19:17, 41.22it/s] 18%|█▊ | 44478/240584 [18:02<1:21:55, 39.89it/s] 18%|█▊ | 44483/240584 [18:02<1:17:51, 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CPU times: user 1h 22min 46s, sys: 22min, total: 1h 44min 46s Wall time: 1h 36min 12s
In [61]:
Out[61]:
True
In [64]:
echoes_heard = []
group_size = []
group_diameter = []
nearest_3nbrs = []
nbr_detection_range = []
uuid = []
seed = []
param_ids = []
parameter_values = []
echo_levels = []
echo_directions = []
for each in extracted_simdata:
if type(each)==list:
for variable, list_to_append in zip(each, [echoes_heard, group_size, group_diameter,
nearest_3nbrs, nbr_detection_range,
uuid, seed, param_ids, parameter_values, echo_levels,
echo_directions]):
list_to_append.append(variable)
else:
print(each)
..//simulations/multivariable_simulations/parameter_space_VM2_6148dbe9-ce56-4fbb-ad71-15f517af0b1b_552997550_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_7714af50-6bfd-40ee-889e-427da1cdda50_594867989_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_8a665819-bc07-4556-b1b1-9a76ce489b2e_730643019_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_20dcaccf-d09b-49b9-9ae2-f4e73601525f_408333549_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_eb58d136-765f-48e3-9721-874f58ba921b_467431177_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_c38079a2-b061-46cc-a034-13a5b74a8da1_1069861845_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_50d1d268-6a7a-44b2-903f-424dd61fe65f_395567271_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_10d687f1-f834-41d1-a51b-dd8bc7f04b55_656116509_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_6e31032a-fcb4-4147-8a34-62500e89e4f3_685883419_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_0fc35f32-30a1-412e-a0ba-30857f2f4aaa_1072090725_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_bbbfa31b-1ec9-43fe-807d-cc243b15576c_480940244_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_f4d1fb6a-d029-4a2e-8cbe-325997ea6ffc_116728248_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_d359ea89-fcb1-47e2-91f6-5f9a25418076_722422830_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_bc626bb7-98a0-4a51-88ea-1c012d9f05c9_372081121_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_11de21f6-cfa3-4c30-bc68-58186fc3b195_1039230363_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_b574c8ed-30c7-4798-853d-7f4f54d8012f_379334546_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_7b5810c4-9af3-4657-bbe6-ea87681da50f_362988286_.simresults ..//simulations/multivariable_simulations/parameter_space_VM2_b50dca8a-4999-4f63-a410-d15ed7b8eb2a_949822252_.simresults
In [65]:
simulation_data = pd.DataFrame(data = {'nbrs_detected':echoes_heard,
'group_size':group_size,
'group_diameter':group_diameter,
'nearest_neighbour_distance':nearest_3nbrs,
'nbrs_detected_distance':nbr_detection_range,
'uuid':uuid,
'seed':seed,
'paramset_id':param_ids,
'parameters_joint':parameter_values,
'echo_levels':echo_levels,
'echo_directions':echo_directions})
In [66]:
simulation_data.head()
Out[66]:
| echo_directions | echo_levels | group_diameter | group_size | nbrs_detected | nbrs_detected_distance | nearest_neighbour_distance | parameters_joint | paramset_id | seed | uuid | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.200637 | 100 | 0 | [] | [1.0738870171204988, 1.2275738371897262, 1.310... | [90, 0.0025, -1, 1.0, 106, 0.05, True] | 90*0.0025*-1*1.0*106*0.05*True | 698146828 | de3d78d0-d71f-44b0-ada9-16fef6f18435 |
| 1 | 2 33.3398 Name: theta, dtype: float64 | 2 85.896433 Name: level, dtype: float64 | 7.200687 | 100 | 1 | [0.6234210867233229] | [0.5040997912106178, 0.5805986746798041, 0.623... | [10, 0.001, 0, 0.5, 120, 0.05, False] | 10*0.001*0*0.5*120*0.05*False | 428387352 | bf86593a-3674-4391-abc1-638cb6f37351 |
| 2 | 2 6.3389 Name: theta, dtype: float64 | 2 56.334859 Name: level, dtype: float64 | 14.180115 | 100 | 1 | [1.2115445731783536] | [1.002187567497149, 1.1047736710966196, 1.2115... | [90, 0.001, -1, 1.0, 94, 0.2, False] | 90*0.001*-1*1.0*94*0.2*False | 442299525 | fdee9447-c0ef-4ce1-80bc-4f3280e1853c |
| 3 | 1 -37.8798 3 169.1262 4 96.9039 5... | 1 50.781637 3 35.344129 4 36.44448... | 14.386090 | 100 | 10 | [1.2068506589447259, 1.4572132244561569, 1.719... | [1.181548533918081, 1.2068506589447259, 1.3046... | [90, 0.001, -1, 1.0, 94, 0.3, True] | 90*0.001*-1*1.0*94*0.3*True | 139245842 | 74a66af3-ee75-4417-be14-7ecbf69af1c0 |
| 4 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.157732 | 100 | 0 | [] | [1.0210001460745612, 1.118407000244618, 1.3868... | [90, 0.0025, -1, 1.0, 112, 0.3, True] | 90*0.0025*-1*1.0*112*0.3*True | 633627683 | 2ae6f99a-6ebb-47aa-afc0-f6c21dd0f6c4 |
In [67]:
column_names = keyword_arguments['variables_to_extract']
for each in column_names:
simulation_data[each] = np.nan
In [68]:
def split_parameters_joint_to_separate_columns(row, col_names):
for colnam, value in zip(col_names, row['parameters_joint']):
row[colnam] = value
return(row)
In [86]:
%time simulation_data = simulation_data.apply(split_parameters_joint_to_separate_columns, 1, col_names=column_names)
CPU times: user 53.5 s, sys: 729 ms, total: 54.2 s Wall time: 52.4 s
In [87]:
simulation_data.head()
Out[87]:
| echo_directions | echo_levels | group_diameter | group_size | nbrs_detected | nbrs_detected_distance | nearest_neighbour_distance | parameters_joint | paramset_id | seed | uuid | heading_variation | echocall_duration | atmospheric_attenuation | min_spacing | source_level | interpulse_interval | implement_shadowing | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.200637 | 100 | 0 | [] | [1.0738870171204988, 1.2275738371897262, 1.310... | [90, 0.0025, -1, 1.0, 106, 0.05, True] | 90*0.0025*-1*1.0*106*0.05*True | 698146828 | de3d78d0-d71f-44b0-ada9-16fef6f18435 | 90 | 0.0025 | -1 | 1.0 | 106 | 0.05 | True |
| 1 | 2 33.3398 Name: theta, dtype: float64 | 2 85.896433 Name: level, dtype: float64 | 7.200687 | 100 | 1 | [0.6234210867233229] | [0.5040997912106178, 0.5805986746798041, 0.623... | [10, 0.001, 0, 0.5, 120, 0.05, False] | 10*0.001*0*0.5*120*0.05*False | 428387352 | bf86593a-3674-4391-abc1-638cb6f37351 | 10 | 0.0010 | 0 | 0.5 | 120 | 0.05 | False |
| 2 | 2 6.3389 Name: theta, dtype: float64 | 2 56.334859 Name: level, dtype: float64 | 14.180115 | 100 | 1 | [1.2115445731783536] | [1.002187567497149, 1.1047736710966196, 1.2115... | [90, 0.001, -1, 1.0, 94, 0.2, False] | 90*0.001*-1*1.0*94*0.2*False | 442299525 | fdee9447-c0ef-4ce1-80bc-4f3280e1853c | 90 | 0.0010 | -1 | 1.0 | 94 | 0.20 | False |
| 3 | 1 -37.8798 3 169.1262 4 96.9039 5... | 1 50.781637 3 35.344129 4 36.44448... | 14.386090 | 100 | 10 | [1.2068506589447259, 1.4572132244561569, 1.719... | [1.181548533918081, 1.2068506589447259, 1.3046... | [90, 0.001, -1, 1.0, 94, 0.3, True] | 90*0.001*-1*1.0*94*0.3*True | 139245842 | 74a66af3-ee75-4417-be14-7ecbf69af1c0 | 90 | 0.0010 | -1 | 1.0 | 94 | 0.30 | True |
| 4 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.157732 | 100 | 0 | [] | [1.0210001460745612, 1.118407000244618, 1.3868... | [90, 0.0025, -1, 1.0, 112, 0.3, True] | 90*0.0025*-1*1.0*112*0.3*True | 633627683 | 2ae6f99a-6ebb-47aa-afc0-f6c21dd0f6c4 | 90 | 0.0025 | -1 | 1.0 | 112 | 0.30 | True |
In [88]:
yyyymmdd = dt.datetime.now()
timestamp = str([yyyymmdd.year,yyyymmdd.month,yyyymmdd.day,yyyymmdd.hour])
In [89]:
##### thanks to https://realpython.com/fast-flexible-pandas/#but-i-heard-that-pandas-is-slow
data_store = pd.HDFStore('all_simulation_data'+timestamp+'.h5')
data_store['simulation_data'] = simulation_data
data_store.close()
/home/thejasvib/anaconda2/envs/theCPN/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2878: PerformanceWarning: your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types [inferred_type->mixed,key->block3_values] [items->['echo_directions', 'echo_levels', 'nbrs_detected_distance', 'nearest_neighbour_distance', 'parameters_joint', 'paramset_id', 'uuid']] exec(code_obj, self.user_global_ns, self.user_ns)
In [90]:
data_stores_in_folder = glob.glob('all_simulation_data*.h5')
In [91]:
data_stores_in_folder
Out[91]:
['all_simulation_data[2019, 9, 26, 14].h5', 'all_simulation_data[2019, 9, 21, 20].h5']
In [92]:
#load the saved data
data_load = pd.HDFStore('all_simulation_data[2019, 9, 26, 14].h5')
simulation_data = data_load['simulation_data']
data_load.close()
In [94]:
simulation_data.head()
Out[94]:
| echo_directions | echo_levels | group_diameter | group_size | nbrs_detected | nbrs_detected_distance | nearest_neighbour_distance | parameters_joint | paramset_id | seed | uuid | heading_variation | echocall_duration | atmospheric_attenuation | min_spacing | source_level | interpulse_interval | implement_shadowing | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.200637 | 100 | 0 | [] | [1.0738870171204988, 1.2275738371897262, 1.310... | [90, 0.0025, -1, 1.0, 106, 0.05, True] | 90*0.0025*-1*1.0*106*0.05*True | 698146828 | de3d78d0-d71f-44b0-ada9-16fef6f18435 | 90 | 0.0025 | -1 | 1.0 | 106 | 0.05 | True |
| 1 | 2 33.3398 Name: theta, dtype: float64 | 2 85.896433 Name: level, dtype: float64 | 7.200687 | 100 | 1 | [0.6234210867233229] | [0.5040997912106178, 0.5805986746798041, 0.623... | [10, 0.001, 0, 0.5, 120, 0.05, False] | 10*0.001*0*0.5*120*0.05*False | 428387352 | bf86593a-3674-4391-abc1-638cb6f37351 | 10 | 0.0010 | 0 | 0.5 | 120 | 0.05 | False |
| 2 | 2 6.3389 Name: theta, dtype: float64 | 2 56.334859 Name: level, dtype: float64 | 14.180115 | 100 | 1 | [1.2115445731783536] | [1.002187567497149, 1.1047736710966196, 1.2115... | [90, 0.001, -1, 1.0, 94, 0.2, False] | 90*0.001*-1*1.0*94*0.2*False | 442299525 | fdee9447-c0ef-4ce1-80bc-4f3280e1853c | 90 | 0.0010 | -1 | 1.0 | 94 | 0.20 | False |
| 3 | 1 -37.8798 3 169.1262 4 96.9039 5... | 1 50.781637 3 35.344129 4 36.44448... | 14.386090 | 100 | 10 | [1.2068506589447259, 1.4572132244561569, 1.719... | [1.181548533918081, 1.2068506589447259, 1.3046... | [90, 0.001, -1, 1.0, 94, 0.3, True] | 90*0.001*-1*1.0*94*0.3*True | 139245842 | 74a66af3-ee75-4417-be14-7ecbf69af1c0 | 90 | 0.0010 | -1 | 1.0 | 94 | 0.30 | True |
| 4 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.157732 | 100 | 0 | [] | [1.0210001460745612, 1.118407000244618, 1.3868... | [90, 0.0025, -1, 1.0, 112, 0.3, True] | 90*0.0025*-1*1.0*112*0.3*True | 633627683 | 2ae6f99a-6ebb-47aa-afc0-f6c21dd0f6c4 | 90 | 0.0025 | -1 | 1.0 | 112 | 0.30 | True |
Were there any repeated seeds ?¶
Every simulation run was executed in parallel with other runs. If I had relied on a common seed to set the simulations I would have gotten multiple repeated simulation outputs! I ended up using a unique 32 integer to seed every simulation run. The 32 bit integer was derived from a uuid value generated at the initiation of the parallel simulation run. Even though uuid's are supposed to be very unique everytime they are generated - the fact that I brought them down to a 32 bit integer to actually set my simulations meant that there may have been some seeds that were repeated.
Repeated seeds are not a problem per se as long as they were repeated across different parameter sets. If the same seed was used to set two simulations with the same parameter set - the results would be identical and one of them must be removed from this dataset.
In [95]:
seeds, count = np.unique(simulation_data['seed'], return_counts=True)
# if every seed was present only once, all seeds would be counted only once
print(np.unique(count))
[1 2]
As expected, there is one seed repeated. Did the simulations with the same seed have the same parameter set ?¶
In [96]:
repeated_seed = seeds[np.argmax(count)]
repeated_seed
Out[96]:
19466971
In [97]:
sims_w_same_seed = simulation_data[simulation_data['seed']==repeated_seed]
In [98]:
sims_w_same_seed
Out[98]:
| echo_directions | echo_levels | group_diameter | group_size | nbrs_detected | nbrs_detected_distance | nearest_neighbour_distance | parameters_joint | paramset_id | seed | uuid | heading_variation | echocall_duration | atmospheric_attenuation | min_spacing | source_level | interpulse_interval | implement_shadowing | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 66132 | 0 -125.6648 1 150.9088 2 -80.6475 3... | 0 35.837728 1 32.007696 2 39.28986... | 14.179821 | 100 | 8 | [1.0413521585101189, 1.1166507384979132, 1.690... | [1.0413521585101189, 1.1166507384979132, 1.690... | [90, 0.001, -2, 1.0, 94, 0.2, False] | 90*0.001*-2*1.0*94*0.2*False | 19466971 | a0f0a16b-452a-4789-b9b3-22a4f066144b | 90 | 0.0010 | -2 | 1.0 | 94 | 0.2 | False |
| 168059 | 0 60.5818 3 -57.3888 Name: theta, dtype: ... | 0 71.052921 3 67.945865 Name: level, dty... | 7.282233 | 100 | 2 | [0.588679149688309, 0.7216056210725502] | [0.588679149688309, 0.6382095625519607, 0.7202... | [10, 0.0025, -2, 0.5, 106, 0.2, False] | 10*0.0025*-2*0.5*106*0.2*False | 19466971 | 6a814ba6-5884-47e1-98a6-11135d934838 | 10 | 0.0025 | -2 | 0.5 | 106 | 0.2 | False |
In [99]:
plt.figure()
plt.plot(simulation_data['min_spacing'],
simulation_data['group_diameter'], '*')
plt.xticks([0.5,1.0], [0.5,1.0]);plt.title('Group diameter, metres')
plt.ylabel('Largest distance between two bats, metres');plt.xlabel('Minimum inter-bat spacing, metres')
plt.grid();plt.yticks(np.arange(7,16),np.arange(7,16));
What is the effective inter-neighbour distance ?¶
In the simulations bats were randomly placed in 2D Poisson disc placement of individuals (Bridson). The Poisson disc sampling method is a fast way to place many points that are randomly distributed while still maintaining a minimum inter-neighbour distance. This method is superior to a purely random placement of points as this results in very sparse or very dense areas.¶
Since the Poisson disc sampling method is random and only defines a minimum neighbour distance, I want to characterise the effective neighbour distance that was obtained.¶
In [100]:
by_minspacing = simulation_data.groupby('min_spacing')
In [101]:
nearest_nbr_dist = {}
for spacing, df in by_minspacing:
nearest_nbr_distances = df['nearest_neighbour_distance'].reset_index(drop=True)
nearest_nbr_dist[spacing] = np.concatenate(nearest_nbr_distances)
In [102]:
nearest_nbr_dist
Out[102]:
{0.5: array([0.50409979, 0.58059867, 0.62342109, ..., 0.50733283, 0.5251015 ,
0.6892922 ]),
1.0: array([1.07388702, 1.22757384, 1.31094146, ..., 1.08079587, 1.52386178,
1.6071532 ])}
In [103]:
nearest_nbr_dists_for_plot = [nearest_nbr_dist[0.5], nearest_nbr_dist[1.0]]
In [104]:
plt.figure(figsize=(8,8))
plt.violinplot(nearest_nbr_dists_for_plot,
showextrema=False)
plt.xticks([1,2],[0.5,1.0],fontsize=15)
medians = map(np.median,nearest_nbr_dists_for_plot)
plt.hlines(medians, [0.8,1.8],[1.2,2.2], label='median')
plt.legend(fontsize=10)
plt.xlabel('Minimum inter-bat spacing in group, metres', fontsize=15)
plt.ylabel('Effective nearest-neighbour \n distances, metres', fontsize=15)
plt.yticks(np.arange(0.25,2.25,0.25), np.arange(0.25,2.25,0.25),fontsize=15);
The effective nearest-neighbour distances, as measured from the three nearest neighbours of the focal bat is:¶
In [105]:
# for 0.5 m minimum spacing:
neighbour_spacing_pctile_50cm = np.percentile(nearest_nbr_dist[0.5], [5,50,95])
neighbour_spacing_pctile_50cm/0.5
Out[105]:
array([1.01529721, 1.18835605, 1.53417138])
In [106]:
# the realised inter-neighbour distances by the ideal (and unachievable) 0.5 m distance
neighbour_spacing_pctile_50cm/0.5
Out[106]:
array([1.01529721, 1.18835605, 1.53417138])
In [107]:
# for 1.0 m minimum spacing:
neighbour_spacing_pctile_100cm = np.percentile(nearest_nbr_dist[1.0], [5,50,95])
neighbour_spacing_pctile_100cm
Out[107]:
array([1.01491067, 1.18829525, 1.53336743])
In [108]:
# the realised inter-neighbour distances by the ideal (and unachievable) 1.0 m distance
neighbour_spacing_pctile_100cm/1.0
Out[108]:
array([1.01491067, 1.18829525, 1.53336743])
We thus see that overall the nearest neighbour distances were within 1 - 1.5 X the minimum spacing distance.¶
In [109]:
# check how many of eac`h parameter set was run:
param_sets, counts = np.unique(simulation_data['paramset_id'], return_counts=True)
In [124]:
# Check if the number of obtained parameter sets matches 1200 - the expected number
len(param_sets)
Out[124]:
1200
In [125]:
# check what the number of runs per parameter set we have :
np.unique(counts)
Out[125]:
array([198, 199, 200, 384, 400])
Even after removing some simulation results that didn't save properly - we see that every parameter set has at least 198 simulation runs, and some parameter sets have more because they have been repeated or run previously.¶
Are all of the parameter sets present in this dataset ?¶
I expect there to be 1200 parameter sets.
In [131]:
simulation_data.head()
Out[131]:
| echo_directions | echo_levels | group_diameter | group_size | nbrs_detected | nbrs_detected_distance | nearest_neighbour_distance | parameters_joint | paramset_id | seed | uuid | heading_variation | echocall_duration | atmospheric_attenuation | min_spacing | source_level | interpulse_interval | implement_shadowing | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.200637 | 100 | 0 | [] | [1.0738870171204988, 1.2275738371897262, 1.310... | [90, 0.0025, -1, 1.0, 106, 0.05, True] | 90*0.0025*-1*1.0*106*0.05*True | 698146828 | de3d78d0-d71f-44b0-ada9-16fef6f18435 | 90 | 0.0025 | -1 | 1.0 | 106 | 0.05 | True |
| 1 | 2 33.3398 Name: theta, dtype: float64 | 2 85.896433 Name: level, dtype: float64 | 7.200687 | 100 | 1 | [0.6234210867233229] | [0.5040997912106178, 0.5805986746798041, 0.623... | [10, 0.001, 0, 0.5, 120, 0.05, False] | 10*0.001*0*0.5*120*0.05*False | 428387352 | bf86593a-3674-4391-abc1-638cb6f37351 | 10 | 0.0010 | 0 | 0.5 | 120 | 0.05 | False |
| 2 | 2 6.3389 Name: theta, dtype: float64 | 2 56.334859 Name: level, dtype: float64 | 14.180115 | 100 | 1 | [1.2115445731783536] | [1.002187567497149, 1.1047736710966196, 1.2115... | [90, 0.001, -1, 1.0, 94, 0.2, False] | 90*0.001*-1*1.0*94*0.2*False | 442299525 | fdee9447-c0ef-4ce1-80bc-4f3280e1853c | 90 | 0.0010 | -1 | 1.0 | 94 | 0.20 | False |
| 3 | 1 -37.8798 3 169.1262 4 96.9039 5... | 1 50.781637 3 35.344129 4 36.44448... | 14.386090 | 100 | 10 | [1.2068506589447259, 1.4572132244561569, 1.719... | [1.181548533918081, 1.2068506589447259, 1.3046... | [90, 0.001, -1, 1.0, 94, 0.3, True] | 90*0.001*-1*1.0*94*0.3*True | 139245842 | 74a66af3-ee75-4417-be14-7ecbf69af1c0 | 90 | 0.0010 | -1 | 1.0 | 94 | 0.30 | True |
| 4 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.157732 | 100 | 0 | [] | [1.0210001460745612, 1.118407000244618, 1.3868... | [90, 0.0025, -1, 1.0, 112, 0.3, True] | 90*0.0025*-1*1.0*112*0.3*True | 633627683 | 2ae6f99a-6ebb-47aa-afc0-f6c21dd0f6c4 | 90 | 0.0025 | -1 | 1.0 | 112 | 0.30 | True |
In [113]:
call_durations = [0.001, 0.0025]
source_levels = [94, 100, 106, 112, 120]
neighbour_spacing = [0.5, 1.0]
implement_shadowing = [True, False]
ipi = [0.025, 0.05, 0.1, 0.2, 0.3]
heading_variation = [10, 90]
atm_abs = [0,-1,-2]
expected_parameter_sets = []
for heading in heading_variation:
for calldurn in call_durations:
for atmos_abs in atm_abs:
for minspacing in neighbour_spacing:
for level in source_levels:
for interpulse in ipi:
for shadowing in implement_shadowing:
parameset = [heading, calldurn, atmos_abs, minspacing,
level, interpulse, shadowing]
paramset_id = join_all_parameters(parameset)
expected_parameter_sets.append(paramset_id)
In [114]:
expected_parameter_sets = set(expected_parameter_sets)
In [115]:
obtained_parameter_sets = set(np.unique(simulation_data['paramset_id']))
In [116]:
len(expected_parameter_sets.intersection(obtained_parameter_sets))
Out[116]:
1200
In [117]:
unseen_parameter_sets = list(expected_parameter_sets - obtained_parameter_sets)
unseen_parameter_sets
Out[117]:
[]
In [118]:
len(list(expected_parameter_sets - obtained_parameter_sets))
Out[118]:
0
Thus, we see that all the expected parameter sets are now present in the final output data.¶
In [132]:
by_spacing = simulation_data.groupby('min_spacing')
neighbours_by_spacing = [ np.array(df['nbrs_detected']) for value, df in by_spacing]
plt.figure()
plt.violinplot(neighbours_by_spacing)
plt.xticks([1,2],[0.5,1.0]); plt.xlabel('Minimum interneighbour-distance, m');
plt.ylabel('Neighbours detected per call emission')
Out[132]:
Text(0,0.5,'Neighbours detected per call emission')
In [133]:
by_heading = simulation_data.groupby('heading_variation')
neighbours_by_spacing = [ np.array(df['nbrs_detected']) for value, df in by_heading]
plt.figure()
plt.violinplot(neighbours_by_spacing)
plt.xticks([1,2],[10,90]); plt.xlabel('Group heading variation, $\circ$');
plt.ylabel('Neighbours detected per call emission')
Out[133]:
Text(0,0.5,'Neighbours detected per call emission')
In [134]:
by_shadowing = simulation_data.groupby('implement_shadowing')
neighbours_by_shadowing = [ np.array(df['nbrs_detected']) for value, df in by_shadowing]
plt.figure()
plt.violinplot(neighbours_by_shadowing)
plt.xticks([1,2],['NO', 'YES']); plt.xlabel('Shadowing implemented');
plt.ylabel('Neighbours detected per call emission')
Out[134]:
Text(0,0.5,'Neighbours detected per call emission')
In [135]:
by_ipi = simulation_data.groupby('interpulse_interval')
neighbours_by_ipi = [ np.array(df['nbrs_detected']) for value, df in by_ipi]
plt.figure()
plt.violinplot(neighbours_by_ipi)
plt.xticks([1,2,3,4,5],[25, 50, 100, 200, 300]); plt.xlabel('Interpulse intervals, milliseconds');
plt.ylabel('Neighbours detected per call emission')
Out[135]:
Text(0,0.5,'Neighbours detected per call emission')
In [136]:
by_sl = simulation_data.groupby('source_level')
neighbours_by_sl = [ np.array(df['nbrs_detected']) for value, df in by_sl]
plt.figure()
plt.violinplot(neighbours_by_sl)
plt.xticks([1,2,3,4,5],[94, 100, 106, 112, 120]); plt.xlabel('Source levels, dB SPL re 20 $\mu Pa$ @1m');
plt.ylabel('Neighbours detected per call emission')
Out[136]:
Text(0,0.5,'Neighbours detected per call emission')
In [137]:
by_duration = simulation_data.groupby('echocall_duration')
neighbours_by_duration = [ np.array(df['nbrs_detected']) for value, df in by_duration]
plt.figure()
plt.violinplot(neighbours_by_duration)
plt.xticks([1,2],[1, 2.5]); plt.xlabel('Call/echo durations, milliseconds');
plt.ylabel('Neighbours detected per call emission')
Out[137]:
Text(0,0.5,'Neighbours detected per call emission')
In [138]:
by_attenuation = simulation_data.groupby('atmospheric_attenuation')
neighbours_by_atmabs = [ np.array(df['nbrs_detected']) for value, df in by_attenuation]
plt.figure()
plt.violinplot(neighbours_by_atmabs)
plt.xticks([1,2,3],[-2, -1, 0]); plt.xlabel('Atmospheric absorption, dB/m');
plt.ylabel('Neighbours detected per call emission')
Out[138]:
Text(0,0.5,'Neighbours detected per call emission')
Which parameter set achieves an 'optimum' rate of neighbour detection ?¶
In [139]:
by_paramid = simulation_data.groupby('paramset_id')
neighbours_by_params = [ np.array(df['nbrs_detected']) for value, df in by_paramid]
all_paramids = [value for value, df in by_paramid]
median_echoes = map(np.median, neighbours_by_params)
plt.figure()
plt.violinplot(neighbours_by_params)
plt.plot(np.arange(1,len(neighbours_by_params)+1),median_echoes)
best_paramset = np.argmax(median_echoes)
plt.plot(best_paramset+1, median_echoes[best_paramset],'*')
#plt.xticks([1,2],[1, 2.5]); plt.xlabel('Call/echo durations, milliseconds');
plt.grid()
plt.ylabel('Neighbours detected per call emission')
Out[139]:
Text(0,0.5,'Neighbours detected per call emission')
In [140]:
all_paramids[best_paramset]
Out[140]:
'10*0.001*-2*0.5*106*0.3*True'
Running regression analyses on the simulation outputs to understand which factors played a major role in the number of neighbours detected :¶
Having done some visualisations - let's now do some regressions to understand how all of the variables contribute overall to the number of echoes a focal bat can actually detect:¶
In [141]:
import statsmodels.api as sm
import statsmodels.formula.api as smf
In [142]:
simulation_data.head()
Out[142]:
| echo_directions | echo_levels | group_diameter | group_size | nbrs_detected | nbrs_detected_distance | nearest_neighbour_distance | parameters_joint | paramset_id | seed | uuid | heading_variation | echocall_duration | atmospheric_attenuation | min_spacing | source_level | interpulse_interval | implement_shadowing | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.200637 | 100 | 0 | [] | [1.0738870171204988, 1.2275738371897262, 1.310... | [90, 0.0025, -1, 1.0, 106, 0.05, True] | 90*0.0025*-1*1.0*106*0.05*True | 698146828 | de3d78d0-d71f-44b0-ada9-16fef6f18435 | 90 | 0.0025 | -1 | 1.0 | 106 | 0.05 | True |
| 1 | 2 33.3398 Name: theta, dtype: float64 | 2 85.896433 Name: level, dtype: float64 | 7.200687 | 100 | 1 | [0.6234210867233229] | [0.5040997912106178, 0.5805986746798041, 0.623... | [10, 0.001, 0, 0.5, 120, 0.05, False] | 10*0.001*0*0.5*120*0.05*False | 428387352 | bf86593a-3674-4391-abc1-638cb6f37351 | 10 | 0.0010 | 0 | 0.5 | 120 | 0.05 | False |
| 2 | 2 6.3389 Name: theta, dtype: float64 | 2 56.334859 Name: level, dtype: float64 | 14.180115 | 100 | 1 | [1.2115445731783536] | [1.002187567497149, 1.1047736710966196, 1.2115... | [90, 0.001, -1, 1.0, 94, 0.2, False] | 90*0.001*-1*1.0*94*0.2*False | 442299525 | fdee9447-c0ef-4ce1-80bc-4f3280e1853c | 90 | 0.0010 | -1 | 1.0 | 94 | 0.20 | False |
| 3 | 1 -37.8798 3 169.1262 4 96.9039 5... | 1 50.781637 3 35.344129 4 36.44448... | 14.386090 | 100 | 10 | [1.2068506589447259, 1.4572132244561569, 1.719... | [1.181548533918081, 1.2068506589447259, 1.3046... | [90, 0.001, -1, 1.0, 94, 0.3, True] | 90*0.001*-1*1.0*94*0.3*True | 139245842 | 74a66af3-ee75-4417-be14-7ecbf69af1c0 | 90 | 0.0010 | -1 | 1.0 | 94 | 0.30 | True |
| 4 | Series([], Name: theta, dtype: float64) | Series([], Name: level, dtype: float64) | 14.157732 | 100 | 0 | [] | [1.0210001460745612, 1.118407000244618, 1.3868... | [90, 0.0025, -1, 1.0, 112, 0.3, True] | 90*0.0025*-1*1.0*112*0.3*True | 633627683 | 2ae6f99a-6ebb-47aa-afc0-f6c21dd0f6c4 | 90 | 0.0025 | -1 | 1.0 | 112 | 0.30 | True |
In [143]:
# make most of the columns categorical because I've done such sparse sampling
regression_rundata = simulation_data.copy()
for column in keyword_arguments['variables_to_extract'] :
regression_rundata[column] = regression_rundata[column].astype('category')
In [144]:
formula = 'nbrs_detected~heading_variation+atmospheric_attenuation \
+implement_shadowing+interpulse_interval+min_spacing+source_level+echocall_duration\
+source_level+atmospheric_attenuation'
Which parameters predict the probability of detecting at least 1 neighbour ?¶
I now reduce the whole problem to whether a neighbour was detected or not! Every simulation give 0 or $\ge$ 0 number of neighbours detected. This will then reduce it to a binomial distribution, where the two states are 'no neighbours detected' and 'at least one neighbour detected'.¶
In [145]:
at_least_one = regression_rundata['nbrs_detected'] > 0
regression_rundata['geq_oneneighbour'] = np.zeros(at_least_one.size)
regression_rundata.loc[at_least_one,'geq_oneneighbour'] = 1
In [146]:
detection = regression_rundata['geq_oneneighbour']
In [147]:
# only presenting the final and reduced model
logit_formula = 'geq_oneneighbour~heading_variation+ \
implement_shadowing+interpulse_interval+\
min_spacing+echocall_duration+source_level+atmospheric_attenuation'
In [148]:
logit_model = smf.logit(logit_formula, data=regression_rundata)
logit_fit = logit_model.fit( maxiter=200)
Optimization terminated successfully.
Current function value: 0.238334
Iterations 10
In [149]:
logit_predict = logit_fit.predict()
In [150]:
logit_resid = detection - logit_predict
Let's build an ROC curve to figure out which thresholds give us the best predictions - and then use it to decide how well the logistic regression model predicts the $P(\geq1 neighbour)$ being detected.¶
In [151]:
from sklearn import metrics
In [152]:
fpr, tpr, thresholds = metrics.roc_curve(detection, logit_predict)
roc_data = pd.DataFrame(data={'tpr':tpr, 'fpr':fpr, 'thresholds':thresholds})
In [153]:
plt.figure()
plt.plot(thresholds, tpr, label='True positive rate')
plt.plot(thresholds, fpr, label='False positive rate')
plt.xlim(0,1)
plt.xlabel('Threshold');plt.ylabel('True & False positive rate')
plt.legend();plt.grid()
Choosing a reasonable threshold to get the confusion matrix from the logistic regression results:¶
The ROC curve above shows us how the false and true positive rates vary as we alter the threshold at which we consider $\geq1$ neighbour detected. The model should have a low false positive rate to be useful, and so I will choose accept a 5% false positive rate. Within the thresholds that satsify this criteria, I will then choose the threshold with the highest true positive rate.
In [154]:
acceptable_fpr = 0.05
rows_w_acceptable_fpr = roc_data[roc_data['fpr']<=acceptable_fpr].reset_index(drop=True)
highest_tpr_row = rows_w_acceptable_fpr['tpr'].argmax()
best_threshold = rows_w_acceptable_fpr.loc[highest_tpr_row,:]['thresholds']
/home/thejasvib/anaconda2/envs/theCPN/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: The current behaviour of 'Series.argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax' will be corrected to return the positional maximum in the future. For now, use 'series.values.argmax' or 'np.argmax(np.array(values))' to get the position of the maximum row. app.launch_new_instance()
In [155]:
best_threshold
Out[155]:
0.7861980084949216
In [156]:
plt.figure(figsize=(8,8))
plt.plot(thresholds, tpr, label='True positive rate')
plt.plot(thresholds, fpr, label='False positive rate')
plt.xlim(0,1)
plt.xticks(fontsize=15);plt.yticks(fontsize=15)
plt.xlabel('Threshold', fontsize=20);plt.ylabel('True/False positive rate',fontsize=20)
plt.legend();plt.grid()
plt.vlines(best_threshold,0,1);
plt.hlines(acceptable_fpr, 0, 1)
plt.text(best_threshold-0.03, 0.5,'Chosen threshold', rotation=90, fontsize=15)
plt.text(0.01,acceptable_fpr+0.01,'Acceptable false positive rate',fontsize=15)
Out[156]:
Text(0.01,0.06,'Acceptable false positive rate')
In [157]:
from sklearn.preprocessing import Binarizer
In [158]:
binarise_probs = Binarizer(best_threshold)
predicted_binary = binarise_probs.fit_transform(logit_predict.reshape(-1,1))
In [159]:
logit_confusion_matrix = metrics.confusion_matrix(detection, predicted_binary)
true_positive, false_positive, true_negative, false_negative = metrics.confusion_matrix(detection,
predicted_binary).ravel()
In [160]:
logit_confusion_matrix
Out[160]:
array([[116038, 6024],
[ 25546, 92958]])
In [161]:
true_positive, false_positive, true_negative, false_negative
Out[161]:
(116038, 6024, 25546, 92958)
In [162]:
def calc_rate_per_column(X):
X_float = np.float64(X)
X_rate = X_float/np.sum(X_float)
return(X_rate)
In [163]:
rate_logit = np.apply_along_axis(calc_rate_per_column, 0, logit_confusion_matrix)
In [164]:
np.around(rate_logit,2)
Out[164]:
array([[0.82, 0.06],
[0.18, 0.94]])
In [165]:
# rows are the predicted values - columns are the observed values
pd.DataFrame(np.around(rate_logit,2))
Out[165]:
| 0 | 1 | |
|---|---|---|
| 0 | 0.82 | 0.06 |
| 1 | 0.18 | 0.94 |
The logistic regression predicting whether $\geq1$ neighbour is heard seems to be a much better model. It predicts the observed data with a 82% true negative and 94% true positive rate. I am currently satisfied with this mdoel - and now let us see which parameters affect the probability of hearing at least one neighbour.¶
In [166]:
np.unique(regression_rundata['interpulse_interval'],return_counts=True)
Out[166]:
(array([0.025, 0.05 , 0.1 , 0.2 , 0.3 ]), array([47993, 47993, 47996, 48584, 48000]))
In [167]:
logit_fit.summary()
Out[167]:
| Dep. Variable: | geq_oneneighbour | No. Observations: | 240566 |
|---|---|---|---|
| Model: | Logit | Df Residuals: | 240551 |
| Method: | MLE | Df Model: | 14 |
| Date: | Thu, 26 Sep 2019 | Pseudo R-squ.: | 0.6561 |
| Time: | 14:50:19 | Log-Likelihood: | -57335. |
| converged: | True | LL-Null: | -1.6672e+05 |
| Covariance Type: | nonrobust | LLR p-value: | 0.000 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Intercept | -4.7023 | 0.074 | -63.204 | 0.000 | -4.848 | -4.556 |
| heading_variation[T.90] | 0.2780 | 0.015 | 18.350 | 0.000 | 0.248 | 0.308 |
| implement_shadowing[T.True] | 0.2823 | 0.015 | 18.632 | 0.000 | 0.253 | 0.312 |
| interpulse_interval[T.0.05] | 3.8054 | 0.072 | 52.493 | 0.000 | 3.663 | 3.947 |
| interpulse_interval[T.0.1] | 6.8435 | 0.073 | 93.520 | 0.000 | 6.700 | 6.987 |
| interpulse_interval[T.0.2] | 9.5244 | 0.076 | 125.589 | 0.000 | 9.376 | 9.673 |
| interpulse_interval[T.0.3] | 11.1568 | 0.079 | 141.761 | 0.000 | 11.003 | 11.311 |
| min_spacing[T.1.0] | -1.1674 | 0.016 | -72.826 | 0.000 | -1.199 | -1.136 |
| echocall_duration[T.0.0025] | -3.5455 | 0.022 | -158.834 | 0.000 | -3.589 | -3.502 |
| source_level[T.100] | 0.0136 | 0.024 | 0.568 | 0.570 | -0.033 | 0.060 |
| source_level[T.106] | 0.0260 | 0.024 | 1.089 | 0.276 | -0.021 | 0.073 |
| source_level[T.112] | 0.0255 | 0.024 | 1.064 | 0.287 | -0.021 | 0.072 |
| source_level[T.120] | -0.0037 | 0.024 | -0.156 | 0.876 | -0.050 | 0.043 |
| atmospheric_attenuation[T.-1] | -0.0051 | 0.018 | -0.274 | 0.784 | -0.041 | 0.031 |
| atmospheric_attenuation[T.0] | 0.0409 | 0.018 | 2.211 | 0.027 | 0.005 | 0.077 |
In [169]:
# Convert log(odds ratio) to odds ratio :
pd.DataFrame(data = {'oddsratio':np.exp(logit_fit.params),'pvalue': np.around(logit_fit.pvalues,2)})
Out[169]:
| oddsratio | pvalue | |
|---|---|---|
| Intercept | 0.009075 | 0.00 |
| heading_variation[T.90] | 1.320484 | 0.00 |
| implement_shadowing[T.True] | 1.326155 | 0.00 |
| interpulse_interval[T.0.05] | 44.941312 | 0.00 |
| interpulse_interval[T.0.1] | 937.739194 | 0.00 |
| interpulse_interval[T.0.2] | 13689.946704 | 0.00 |
| interpulse_interval[T.0.3] | 70035.497267 | 0.00 |
| min_spacing[T.1.0] | 0.311175 | 0.00 |
| echocall_duration[T.0.0025] | 0.028855 | 0.00 |
| source_level[T.100] | 1.013676 | 0.57 |
| source_level[T.106] | 1.026377 | 0.28 |
| source_level[T.112] | 1.025782 | 0.29 |
| source_level[T.120] | 0.996287 | 0.88 |
| atmospheric_attenuation[T.-1] | 0.994953 | 0.78 |
| atmospheric_attenuation[T.0] | 1.041722 | 0.03 |
A note on the final and only model:¶
I have not attempted any kind of model selection/reduction as the intention of the regression analyses is to quantify the extent to which each variable contributes to neighbour detection. Moreover the simulations themselves are a result of all variables being altered intentionally - and so I chose to keep them final model (Bolker et 2009) even at the cost of less precise predictions.
Reference:¶
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J. S. S. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in ecology & evolution, 24(3), 127-135.
Interpreting the model :¶
Parameters explained in descending order of their odds ratios :
1. Inter pulse intervals play a strong role :¶
The interpulse interval (ipi) is duration of time between the end of one call and the start of the next call. The longer the interpulse interval, the longer a bat waits between two calls. In the simulations, every bat had the same interpulse interval - and we see that the longer the time bats wait between calls - the better they are able to detect their neighbours. The reference group here was $interpulse \ interval = 25 milliseconds$, and we see dramatic odds ratios as the interpulse interval is increased first to 50 and then 100 milliseconds. The odds ratio of detecting a neighbour increases 45 (at 50ms ipi) to 70,035 (300 ms ipi) times the reference of 25 ms ipi.
2. Acoustic shadowing promotes neighbour detection :¶
Here I refer to acoustic shadowing as the decrease in sound pressure level of a sound as it passes obstacles. In a group of bats, the neighbouring bats centred around a focal bat are the obstacles. The reference group ('implement_shadowing') is the set of simulations without acoustic shadowing implemented - where every bat is 'transparent' when sounds pass through them. The odds ratio increases 1.32 times by implementing acoustic shadowing.
3. Heading variation in a group increases neighbour detection :¶
This parameter describes the amount of variation in the direction that individuals are pointing their beams at. The reference group is $heading \ variation = 10 ^\circ$ and we see that a group with $heading \ variation = 90 ^\circ$ has a 1.32 time increase in detecting $\geq1$ neighbour. This implies that a group engaging in behaviours where there is low alignment of bat scanning/flight directions could actually be advantageous.
4. Bats flying closer to each other can detect each other better :¶
The minimum spacing between two adjacent bats was set to be either 0.5 or 1.0 meters. When bats were placed further from each other (1m minimum spacing) they detected each other better in the midst of the loud calls, than when they were further apart (0.5m minimum spacing). The reference group is 0.5m minimum spacing, and we see the odds ratio drops to 0.3 at 1.0m minimum spacing between bats.
5. Shorter calls allow better detection of neighbours :¶
Calls were set to be either 1ms or 2.5ms long. The reference was the neighbour detection at 1ms call duration. A longer call of 2.5 ms led to the odds ratio dropping to ~ 0.03 times the reference group.
6. Call source levels do not affect neighbour detection :¶
All bats in a group were simulated to emit calls at one of 94, 100, 106, 112 or 120 dB SPL re 20 $\mu$Pa at 1m. The regressions results show mixed and very small variations in odds ratio was between 0.99-1.03 times the reference group. The reference group was all bats emitting at 94 dB SPL re 20 $\mu$Pa at 1m. Since all bats were emitting at the same source level - there was no change in echo to masker levels and thus source levels do not contribute to much variation in the detection of neighbours.
7. Atmospheric attenuation :¶
The atmospheric attenuation varied between 0, -1 and -2 dB/m. The reference group was -2 dB/m. The odds ratio of neighbour detection for different atmospheric absorption rates lies between 0.99-1.04. Variation in atmospheric attenuation is also not expected to change neighbour detection because there is no effective change in the echo-masker levels. The effect of atmospheric absorption is applicable to both echoes and maskers equally.
Regression Analyses that did not explain the data as well - but nonetheless showed similar trends :¶
To be transparent - I present here the other regression models that I tried to fit the data to - but which were not as succesful in their predictions.
In [170]:
regression_rundata.columns
Out[170]:
Index([u'echo_directions', u'echo_levels', u'group_diameter', u'group_size',
u'nbrs_detected', u'nbrs_detected_distance',
u'nearest_neighbour_distance', u'parameters_joint', u'paramset_id',
u'seed', u'uuid', u'heading_variation', u'echocall_duration',
u'atmospheric_attenuation', u'min_spacing', u'source_level',
u'interpulse_interval', u'implement_shadowing', u'geq_oneneighbour'],
dtype='object')
In [171]:
formula = 'nbrs_detected~heading_variation+ \
implement_shadowing+interpulse_interval+\
min_spacing+echocall_duration + source_level + atmospheric_attenuation'
In [172]:
model_num_echoes = smf.glm(formula, data=regression_rundata,
family = sm.families.NegativeBinomial(sm.families.links.log))
/home/thejasvib/anaconda2/envs/theCPN/lib/python2.7/site-packages/ipykernel/__main__.py:2: DeprecationWarning: Calling Family(..) with a link class as argument is deprecated. Use an instance of a link class instead. from ipykernel import kernelapp as app
In [173]:
negbinom_fit = model_num_echoes.fit()
In [174]:
negbinom_fit.aic
Out[174]:
621148.370513462
In [175]:
model_num_echoes.fit().summary()
Out[175]:
| Dep. Variable: | nbrs_detected | No. Observations: | 240566 |
|---|---|---|---|
| Model: | GLM | Df Residuals: | 240551 |
| Model Family: | NegativeBinomial | Df Model: | 14 |
| Link Function: | log | Scale: | 1.0000 |
| Method: | IRLS | Log-Likelihood: | -3.1056e+05 |
| Date: | Thu, 26 Sep 2019 | Deviance: | 78838. |
| Time: | 14:50:22 | Pearson chi2: | 1.00e+05 |
| No. Iterations: | 10 | ||
| Covariance Type: | nonrobust |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Intercept | -4.8268 | 0.071 | -67.878 | 0.000 | -4.966 | -4.687 |
| heading_variation[T.90] | 0.0309 | 0.006 | 4.855 | 0.000 | 0.018 | 0.043 |
| implement_shadowing[T.True] | 0.0908 | 0.006 | 14.243 | 0.000 | 0.078 | 0.103 |
| interpulse_interval[T.0.05] | 3.5390 | 0.072 | 49.366 | 0.000 | 3.399 | 3.680 |
| interpulse_interval[T.0.1] | 5.3725 | 0.071 | 75.852 | 0.000 | 5.234 | 5.511 |
| interpulse_interval[T.0.2] | 6.6790 | 0.071 | 94.473 | 0.000 | 6.540 | 6.818 |
| interpulse_interval[T.0.3] | 7.3326 | 0.071 | 103.747 | 0.000 | 7.194 | 7.471 |
| min_spacing[T.1.0] | -0.5077 | 0.006 | -79.252 | 0.000 | -0.520 | -0.495 |
| echocall_duration[T.0.0025] | -1.4501 | 0.007 | -219.066 | 0.000 | -1.463 | -1.437 |
| source_level[T.100] | 0.0034 | 0.010 | 0.334 | 0.738 | -0.016 | 0.023 |
| source_level[T.106] | 0.0042 | 0.010 | 0.411 | 0.681 | -0.016 | 0.024 |
| source_level[T.112] | 0.0045 | 0.010 | 0.446 | 0.656 | -0.015 | 0.024 |
| source_level[T.120] | 0.0003 | 0.010 | 0.029 | 0.977 | -0.019 | 0.020 |
| atmospheric_attenuation[T.-1] | -0.0036 | 0.008 | -0.465 | 0.642 | -0.019 | 0.012 |
| atmospheric_attenuation[T.0] | 0.0013 | 0.008 | 0.160 | 0.872 | -0.014 | 0.017 |
In [176]:
predictions = model_num_echoes.fit().predict()
observed = np.array(regression_rundata['nbrs_detected'])
residue = observed - predictions
In [177]:
plt.figure()
plt.hist(residue);
In [178]:
prediction_error, occurence = np.unique(residue, return_counts=True)
occurence = np.float64(occurence)
occurence /= np.sum(occurence)
In [179]:
plt.figure()
plt.plot(observed+np.random.normal(0,0.05,observed.size),
predictions, '*')
plt.xlabel('Observed number of neighbours detected')
plt.ylabel('Predicted number of neighbours detected')
Out[179]:
Text(0,0.5,'Predicted number of neighbours detected')
In [180]:
negbinom_confusion_matrix = metrics.confusion_matrix(observed, np.around(predictions))
In [181]:
negbinom_rates = calc_rate_per_column(negbinom_confusion_matrix)
In [182]:
fig, ax = plt.subplots()
heatmap = ax.imshow(negbinom_rates)
fig.colorbar(heatmap)
ax.set_xticks(np.arange(negbinom_rates.shape[0]))
ax.set_xticklabels(np.unique(observed), rotation=75);
ax.xaxis.tick_top()
ax.set_yticks(np.arange(negbinom_rates.shape[0]))
ax.set_yticklabels(np.unique(observed));
plt.xlabel('Observed : Detected neighbours \n True positive rates of prediction', fontsize=15)
plt.ylabel('Predicted : Detected neighbours', fontsize=15)
Out[182]:
Text(0,0.5,'Predicted : Detected neighbours')
We can see that the predictions are correct highest at predicting when 0 neighbours are detected - and that too only 50% of the time. The negative binomial model isn't doing a very great job at predicting the number of neighbours detected.¶
In [183]:
plt.figure()
plt.plot(np.diag(negbinom_rates));plt.ylabel('True positive rates')
plt.xticks(range(np.unique(observed).size), np.unique(observed))
plt.xlabel('Observed number of neighbours detected');plt.grid()
In [184]:
plt.figure()
plt.hist(observed);
plt.grid()
plt.xlabel('Number of neighbours detected', fontsize=15)
plt.ylabel('Occurence, number of simulations', fontsize=15)
Out[184]:
Text(0,0.5,'Occurence, number of simulations')
The histogram shows a preponderance of zeroes in the dataset. Let us use zero-inflated Poisson models to handle the dataset.¶
In [185]:
# folllowing https://bryansweber.com/2018/10/26/python-and-zero-inflated-models/ accessed 14-09-2019
import statsmodels.discrete.count_model as reg_models
In [186]:
modelw_zip = reg_models.ZeroInflatedPoisson.from_formula(formula, regression_rundata)
In [187]:
zipmodel_fit = modelw_zip.fit()
/home/thejasvib/anaconda2/envs/theCPN/lib/python2.7/site-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals "Check mle_retvals", ConvergenceWarning)
Warning: Maximum number of iterations has been exceeded.
Current function value: 1.116698
Iterations: 35
Function evaluations: 37
Gradient evaluations: 37
/home/thejasvib/anaconda2/envs/theCPN/lib/python2.7/site-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals "Check mle_retvals", ConvergenceWarning)
In [188]:
zipmodel_predict = zipmodel_fit.predict()
In [189]:
zipmodel_residues = observed - zipmodel_predict
In [190]:
plt.figure()
plt.hist(zipmodel_residues);
In [191]:
plt.figure()
plt.plot(observed-0.25+np.random.normal(0,0.01,observed.size),
zipmodel_residues, '*', alpha=0.3,
label='zero inflated poisson')
plt.xlabel('Number of echoes heard (Y variable) ')
plt.ylabel('Error in prediction, (Observed-predicted)')
plt.grid()
plt.legend()
Out[191]:
<matplotlib.legend.Legend at 0x7f17025d2d10>
In [192]:
plt.figure()
plt.plot(observed+np.random.normal(0,0.05,observed.size),
zipmodel_predict, '*')
plt.plot(np.arange(5),np.arange(5),'-*',label='Ideal fit')
plt.xlabel('Observed number of neighbours detected')
plt.ylabel('Predicted number of neighbours detected')
plt.legend();plt.grid()
In [193]:
# calculate the confusion matrix for the zero inflated Poisson
zip_confusion_matrix = metrics.confusion_matrix(observed, np.around(zipmodel_predict))
zip_prediction_rates = calc_rate_per_column(zip_confusion_matrix)
In [194]:
# rate of true positives in the predictions
fig, ax = plt.subplots()
heatmap = ax.imshow(zip_prediction_rates)
fig.colorbar(heatmap)
ax.set_xticks(np.arange(zip_prediction_rates.shape[0]))
ax.set_xticklabels(np.unique(observed), rotation=75);
ax.xaxis.tick_top()
ax.set_yticks(np.arange(zip_prediction_rates.shape[0]))
ax.set_yticklabels(np.unique(observed));
plt.xlabel('Observed : Detected neighbours \n True positive rates of prediction', fontsize=15)
plt.ylabel('Predicted : Detected neighbours', fontsize=15)
/home/thejasvib/anaconda2/envs/theCPN/lib/python2.7/site-packages/matplotlib/pyplot.py:537: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). max_open_warning, RuntimeWarning)
Out[194]:
Text(0,0.5,'Predicted : Detected neighbours')
Even the zero-inflated Poisson model doesn't predict the observed data too well. At its best the cases of zero neighbours detected are predicted, but that too only about 50% of the time.¶
The negative binomial and zero-inflated Poisson models:¶
In [195]:
zipmodel_fit.summary()
Out[195]:
| Dep. Variable: | nbrs_detected | No. Observations: | 240566 |
|---|---|---|---|
| Model: | ZeroInflatedPoisson | Df Residuals: | 240551 |
| Method: | MLE | Df Model: | 14 |
| Date: | Thu, 26 Sep 2019 | Pseudo R-squ.: | 0.4601 |
| Time: | 14:50:41 | Log-Likelihood: | -2.6864e+05 |
| converged: | False | LL-Null: | -4.9756e+05 |
| Covariance Type: | nonrobust | LLR p-value: | 0.000 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| inflate_const | -8.1326 | 0.388 | -20.938 | 0.000 | -8.894 | -7.371 |
| Intercept | -4.8584 | 0.071 | -68.158 | 0.000 | -4.998 | -4.719 |
| heading_variation[T.90] | -0.0016 | 0.003 | -0.565 | 0.572 | -0.007 | 0.004 |
| implement_shadowing[T.True] | 0.0561 | 0.003 | 19.761 | 0.000 | 0.051 | 0.062 |
| interpulse_interval[T.0.05] | 3.6014 | 0.072 | 49.937 | 0.000 | 3.460 | 3.743 |
| interpulse_interval[T.0.1] | 5.4607 | 0.071 | 76.581 | 0.000 | 5.321 | 5.600 |
| interpulse_interval[T.0.2] | 6.6846 | 0.071 | 93.886 | 0.000 | 6.545 | 6.824 |
| interpulse_interval[T.0.3] | 7.2678 | 0.071 | 102.105 | 0.000 | 7.128 | 7.407 |
| min_spacing[T.1.0] | -0.4679 | 0.003 | -160.315 | 0.000 | -0.474 | -0.462 |
| echocall_duration[T.0.0025] | -1.2996 | 0.003 | -376.151 | 0.000 | -1.306 | -1.293 |
| source_level[T.100] | 0.0023 | 0.004 | 0.516 | 0.606 | -0.006 | 0.011 |
| source_level[T.106] | -0.0026 | 0.004 | -0.584 | 0.559 | -0.011 | 0.006 |
| source_level[T.112] | 1.55e-06 | 0.004 | 0.000 | 1.000 | -0.009 | 0.009 |
| source_level[T.120] | -0.0037 | 0.004 | -0.824 | 0.410 | -0.012 | 0.005 |
| atmospheric_attenuation[T.-1] | -0.0042 | 0.003 | -1.208 | 0.227 | -0.011 | 0.003 |
| atmospheric_attenuation[T.0] | -0.0026 | 0.003 | -0.763 | 0.445 | -0.009 | 0.004 |
In [196]:
negbinom_fit.summary()
Out[196]:
| Dep. Variable: | nbrs_detected | No. Observations: | 240566 |
|---|---|---|---|
| Model: | GLM | Df Residuals: | 240551 |
| Model Family: | NegativeBinomial | Df Model: | 14 |
| Link Function: | log | Scale: | 1.0000 |
| Method: | IRLS | Log-Likelihood: | -3.1056e+05 |
| Date: | Thu, 26 Sep 2019 | Deviance: | 78838. |
| Time: | 14:50:41 | Pearson chi2: | 1.00e+05 |
| No. Iterations: | 10 | ||
| Covariance Type: | nonrobust |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Intercept | -4.8268 | 0.071 | -67.878 | 0.000 | -4.966 | -4.687 |
| heading_variation[T.90] | 0.0309 | 0.006 | 4.855 | 0.000 | 0.018 | 0.043 |
| implement_shadowing[T.True] | 0.0908 | 0.006 | 14.243 | 0.000 | 0.078 | 0.103 |
| interpulse_interval[T.0.05] | 3.5390 | 0.072 | 49.366 | 0.000 | 3.399 | 3.680 |
| interpulse_interval[T.0.1] | 5.3725 | 0.071 | 75.852 | 0.000 | 5.234 | 5.511 |
| interpulse_interval[T.0.2] | 6.6790 | 0.071 | 94.473 | 0.000 | 6.540 | 6.818 |
| interpulse_interval[T.0.3] | 7.3326 | 0.071 | 103.747 | 0.000 | 7.194 | 7.471 |
| min_spacing[T.1.0] | -0.5077 | 0.006 | -79.252 | 0.000 | -0.520 | -0.495 |
| echocall_duration[T.0.0025] | -1.4501 | 0.007 | -219.066 | 0.000 | -1.463 | -1.437 |
| source_level[T.100] | 0.0034 | 0.010 | 0.334 | 0.738 | -0.016 | 0.023 |
| source_level[T.106] | 0.0042 | 0.010 | 0.411 | 0.681 | -0.016 | 0.024 |
| source_level[T.112] | 0.0045 | 0.010 | 0.446 | 0.656 | -0.015 | 0.024 |
| source_level[T.120] | 0.0003 | 0.010 | 0.029 | 0.977 | -0.019 | 0.020 |
| atmospheric_attenuation[T.-1] | -0.0036 | 0.008 | -0.465 | 0.642 | -0.019 | 0.012 |
| atmospheric_attenuation[T.0] | 0.0013 | 0.008 | 0.160 | 0.872 | -0.014 | 0.017 |